face recognition with occlusion github

For example, in face recognition tasks the … The face we make (www.thefacewemake.org) • Frontal view faces of strangers on the streets, captured under totally uncontrolled conditions in real environment • Face images are not well registered • Includes hand occlusion which is difficult to be detected by skin colour based models Face Recognition with Occlusion … Deep Learning Face Representation from Predicting 10,000 Classes. While these do affect detection and recognition, there is no clear distinction between scene–dependent chal-lenges like occlusion, illumination, etc., and the challenges imposed by traditional notions of “quality impairments” from This is the strategy that we also employ in this work. Face Recognition by Sparse Representation [book chapter] John Wright, Allen Yang, Arvind Ganesh, Andrew Wagner, Zihan Zhou, and Yi Ma. However, these linear representation based feature learning methods ignore the invariance of the features. 2, FEBRUARY 2017 797 Adaptive Cascade Regression Model for Robust Face Alignment Qingshan Liu, Senior Member, IEEE, Jiankang Deng, Jing Yang, Guangcan Liu, Member, IEEE, and Dacheng Tao, Fellow, IEEE Abstract—Cascade regression is a popular face alignment approach, and it has achieved good performances on the wild Fusing Robust Face Region Descriptors via Multiple Metric Learning for Face Recognition in the Wild Zhen Cui1;3, Wen Li2, Dong Xu2, Shiguang Shan1, Xilin Chen1 1Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of … This work was focused on solving the partial occlusion problem in face recognition. The file bbox means bounding box, which is obtained by our detector for training/validation sets. Her recent research interests lie in face super-resolution, face recognition and 3D face modeling, Retina recognition, expression recognition with applications to finance and intelligent pain … While general face recognition has garnered a lot of works over the past five years. Exten-sive experiments on Multi-PIE and LFW demonstrate that the proposed method significantly improves face recogni-tion performance and outperforms state-of-the-art methods in both constrained and unconstrained environments. Face recognition technology resistant to harsh lighting conditions and occlusions, allowing accurate recognition of single and multiple faces. How to run: The bounding box often excludes part of forehead and even part of chin sometimes. The limited generalization of neural networks is a critical problem for artificial intelligence, in applications ranging from automated driving and biomedical image analysis, and domains like reinforcement learning, control, and representational theory. Face Recognition under Varying Illumination, Pose and Contiguous Occlusion M.S. (Taigman et al. We present an implementation of our medial-axis and face-based 3D Pose Recognition Algorithm. Special Issue: Face Recognition and Spoofing Attacks Strengths and weaknesses of deep learning models for face recognition against image degradations ISSN 2047-4938 Received on 17th May 2017 Revised 14th August 2017 Accepted on 7th September 2017 E-First on 24th October 2017 doi: 10.1049/iet-bmt.2017.0083 www.ietdl.org More than 65 million people use GitHub to discover, fork, and contribute to over 200 million projects. We thus introduce a novel robust nuclear norm regularized regression (RNR) method for face recognition with occlusion. This paper discusses a method on developing a MATLAB-based Convolutional Neural Network (CNN) face recognition system with Graphical User Interface (GUI) as the user input. This paper. SRC is robust to occlusion, illumination and noise, and achieves excellent performance. These methods require Methods for face recognition across pose can broadly be classified into 2D and 3D techniques. Although it sounds like a very simple task for us, it has proven to be a complex task for a computer, as it has many variables that can impair the accuracy of the methods, for example: illumination variation, low resolution, occlusion, amongst other. However, the documentation for Azure Face API is in C#. TinyFace Dataset and Evaluation Codes: (148MB): [Google Drive] [Baidu Cloud] Contact. A face recognition system typically has two main stages: a face repre-sentation stage and a face matching stage [3]. SRC is robust to occlusion, illumination and noise, and achieves excellent performance. A framework employing multiple convolutional networks Patch-gated cnn for occlusion-aware facial expression recognition Is there other method for address occlusion problem? Please feel free to send any questions and/or comments to Zhiyi Cheng at z.cheng@qmul.ac.ukz.cheng@qmul.ac.uk 3D-Aided Deep Pose-Invariant Face Recognition. More than 65 million people use GitHub to discover, fork, and contribute to over 200 million projects. Practical systems, or even manual face cropping, may bring consider-able image misalignments, including translations, scaling and Unsupervised Face Normalization With Extreme Pose and Expression in the Wild ; GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction ; HF-PIM: Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization ; Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs The extended database as opposed to the original Yale Face Database B with 10 subjects was first reported by Kuang-Chih Lee, Jeffrey Ho, and David Kriegman in "Acquiring Linear Subspaces for Face Recognition under Variable Lighting, PAMI, May, 2005 ." When Face Recognition Meets Occlusion: A New Benchmark More general and extensive reviews can be found in other surveys [18,19,20] and books [21,22, 80 23,24]. Occlusion problem is common for iris recognition, almost all existing CNN-based methods adopt the masking strategy. First, you’ll solve the segmentation problem by finding the largest face in an image. substantial advances for face representation learning [6–8]. Experiments on benchmark face databases demonstrate the effectiveness and robustness of our method, which outperforms state-of-the-art methods. Face and Medial-Axis 3D Pose Recognition (FAMrec) Implements object pose estimation for arbitrary geomtries (.stl) to a scene described by a point cloud. ... For the whole code, you can visit my Github profile. The winner proposed a scene feature extractor and a series of face … As an example, a criminal in China was caught because a Face Recognition system in a mall detected his face and raised an alarm. on all parts of the face except for the nose, which made sense given that small changes in non-nose regions tend to correspond to emotion changes. Face representation aims to extract a set of discriminative features so that the face … If it's not, then the part is occlusion. Occlusion-free Face Alignment: Deep Regression Networks Coupled with De-corrupt AutoEncoders Jie Zhang, Meina Kan, Shiguang Shan, Xilin Chen. 2016. I received my Ph.D. degree in CUHK Multimedia Laboratory in 2020, supervised by Chen Change Loy and Xiaoou Tang.Before that, I received my B.E. We need haar cascade frontal face recognizer to detect the face from our webcam. Download. View on GitHub Overview. However, even the gallery images of the face recognition systems can be captured with RGB-D sensors to retain more subject discriminative infor- 1. using inpainting function. LFW database (Huang et al. [22] pro-posed an extended SRC to handle variations of faces in illumination, alignment, pose, and occlusion. thesis, University of Illinois at Urbana-Champaign, 2010. 2016. for face recognition with large samples are used to assist learning a robust feature representation generally. Unsupervised Face Normalization with Extreme Pose and Expression in the Wild Yichen Qian12, Weihong Deng1∗, Jiani Hu1 1Beijing University of Posts and Telecommunications 2AI Labs, Didi Chuxing, Beijing 100193, China {mx54039q, whdeng, jnhu}@bupt.edu.cn Abstract Face recognition achieves great success thanks to the The existing face recognition datasets usually lack occlusion samples, which hinders the development of face recognition. success in face recognition, it requires the atoms in the dictionary to be well aligned for a reconstruction purpose, which is not always satisfied. The problem of 3D face reconstruction from stills and video is a hot research topic across Computer Vision and Computer Graph-ics with foundations in face recognition [Blanz and Vetter 2003; Tran et al. Current Methods: Challenges Face often turned away Bodies prone to heavy occlusion and overlap. Interests in recognition of faces with occlusions have been fairly less, with limited focus on generalization study of state-of-the-art face recognition models for occluded faces. Face recognition using matlab. Face API can detect human faces in an image and return the … The research on face recognition still continues after several decades since the study of this biometric trait exists. Moreover, 3D face recognition systems could accurately recognize human faces even under dim lights and with variant facial positions and expressions, in … Though great strides have been made in this field (see Sec. Face alignment, which refers to facial landmark detec-tion in this work, serves as a key step for many face appli-cations, e.g., face recognition [76], face verification [49, 50] and face frontalisation [21]. Face recognition is one of the most sought-after technologies in the field of machine learning. For this Project, the important take away from the paper is the idea of representing a face as a 128-dimensional embedding. Most traditional low-level cues based approaches synthesize the contents by searching patches from the known region of the same image [ 2 , 17 , 28 ] . Deep face expression deformation. I don't know whether or not this method is the state-of-the-art method. (a) No Occlusion (b) Occlusion Fig. After localizing the eyes and aligning the face, divide the face two parts-left one and right one. If the Face resource you created in the Prerequisites section deployed successfully, click the Go to Resource button under Next Steps.You can find your key and endpoint in the resource's key and endpoint page, under resource management.. 1. Learning from the Past: Meta-Continual Learning with Knowledge Embedding for Jointly Sketch, Cartoon, and Caricature Face Recognition. ing, occlusion, expression, head shape, etc., that affect “face quality”. Face recognition is a personal identification system that uses personal characteristics of a ... the possibility of partial occlusion and disguise. Using inpaint to restore old photo. We make major improvements in the iris segmentation phase. to fill the invisible region caused by self occlusion. Therefore, face recognition based on deep learning can greatly improve the First and foremost are large pose and occlusion problems, which can even result in more than 10% performance degradation. We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. ... results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers. MegaFace. The mask must be 8-bit 1-channel image in function 'icvInpaint' However, the conventional algorithms of face recognition are susceptible to multiple conditions, such as lighting, occlusion, viewing angle or camera rotation. 2016], as well as core applications in face It inherits advantages from traditional 2D face recognition, such as the natural recognition process and a wide range of applications. To this end, we pioneer a simulated occlusion face recognition dataset. Enhanced Blind Face Restoration With Multi-Exemplar Images and Adaptive Spatial Feature Fusion (ASFFNet) Xiaoming Li, Wenyu Li, Dongwei Ren, Hongzhi Zhang, Meng Wang and Wangmeng Zuo. 3.2.1 Robustness to occlusion Current approaches struggle under occlusion because they do not treat it in a principled way. The first (of many more) face detection datasets of human faces especially created for face detection (finding) instead of recognition: BioID Face Detection Database 1521 images with human faces, recorded under natural conditions, i.e. trend is to attempt direct recognition [3, 4] by comparing re-blurred versions from the gallery with the blurred probe image in the Local Binary Pattern (LBP) [11] space. READ PAPER. Practical face recog-nition algorithms must also possess the ability to recognize faces across reasonable variations in pose. Thank you. Occlusion-free Face Alignment: Deep Regression Networks Coupled with De-corrupt AutoEncoders. Conditional Convolutional Neural Network for Modality-aware Face Recognition Chao Xiong 1, Xiaowei Zhao , Danhang Tang , Karlekar Jayashree3, Shuicheng Yan2, and Tae-Kyun Kim1 1Department of Electrical and Electronic Engineering, Imperial College London 2Department of Electrical and Computer Engineering, National University of Singapore 3Panasonic R&D Center Singapore Modifying inpainting to achieve proper blur. [4]. WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. In order to perform face recognition operations such as Identify and Find Similar, Face API customers need to create an assorted list of Person objects. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Oral). If you like the content and want more as such, follow me for same. Occlusion and pose variations, which can change facial appearance significantly, are two major obstacles for automatic Facial Expression Recognition (FER). In this paper, we propose a new iris recognition method based on a robust iris segmentation approach for improving iris recognition performance. Designing and developing an occlusion based face detector ... For writing our code we will be using face_recognition.py named file. 1. Facial Expression Recognition with Deep Learning Amil Khanzada (amilkh@stanford.edu), Charles Bai (cbai@stanford.edu), Ferhat Turker Celepcikay (turker@stanford.edu) Motivation & Objectives Face detection has been made insufficient progress in the VR games field, due to the lack of database of VR games. The large pose discrepancy between two face images is one of the fundamental challenges in automatic face recognition. ... More suitable face alignment is promising for better recognition accuracy. Face recognition techniques have been developed significantly in recent years. Liu et al. More specifically following are some of the challenges in occluded face recognition Keywords: Facial expression recognition, occlusion, face mask, deep attentional network, generative ad- versarial network, facial landmark detection, CO VID-19 1. Use this article to stay up to date with feature enhancements, fixes, and documentation updates. 8 Face Recognition “Face Recognition is the task of identifying an already detected face as a KNOWN or UNKNOWN face, and in more advanced cases, TELLING EXACTLY WHO’S IT IS ! Face Recognition, Landmark and Relevant Other Feature Extraction ... RetinaFace tend to fail. Low-Resolution Face Recognition Zhiyi Cheng 1, Xiatian Zhu2, and Shaogang Gong 1 School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK fz.cheng,s.gongg@qmul.ac.uk 2 Vision Semantics Ltd., London, UK eddy@visionsemantics.com Abstract. sunglasses, scarf, mask etc. Facial expressions of emotion are a major channel in our daily communications, and it has been subject of intense research in recent years. Face detection is a key link of subsequent face-related applications, such as face recognition , facial expression recognition , and face hallucination , because its effect directly affects the subsequent applications performance. ReID: From Face to Person Person Re-Identification Applications Tracking in a single camera Tracking across multiple cameras Searching a person in a set of videos Clustering persons in a set of photos Challenges Inaccurate detection Misalignment Illumination difference Occlusion Traditional CNN-based face recognition models trained on existing datasets are almost ineffective for heavy occlusion. Face tracking of multiple head positions (smooth head position tracking in real time, eye-gaze and eye closure tracking). Moreover, 3D face recognition systems could accurately recognize human faces even under dim lights and with variant facial positions and expressions, in … Methods for face recognition across pose can broadly be classified into 2D and 3D techniques. face occlusion dataset, YFCC100M-HNfc6 dataset is a deep features extracted from the Yahoo Flickr Creative Commons 100M (YFCC100M) dataset created in 2014 as part of the Yahoo Webscope program. occlusion statistics, grouped in 9 zones. Advisor: Prof. Yi Ma. While (2D) facial landmarks can be used for facial recognition we normally use dedicated facial recognition algorithms for 2D face recognition, including Eigenfaces, Fisherfaces, and LBPs for face recognition. 2007): The LFW database contains 13,233 face images of 5749 subjects collected from internet and has been widely used for unconstrained face recognition with variations of pose, illumination, expression, misalignment and occlusion, and so on. You can choose which AI model to use to extract data from the detected face(s). Nevertheless, the clean feature representation cannot represent the holistic face due to the missing semantic features. Though automatic FER has made substantial progresses in the past few decades, occlusion-robust and pose-invariant issues of FER have received relatively less attention, especially in real-world scenarios. We propose to incorpo- Building a Face Recognition App in Ionic using Imgur and Microsoft Azure! Towards Pose Invariant Face Recognition in the Wild. to fill the invisible region caused by self occlusion. Eyes and eyebrows are most often occluded (hair, hats, sunglasses), as well as center of the face (object interactions). 736-743). Extensive experimental evaluation clearly Exten-sive experiments on Multi-PIE and LFW demonstrate that the proposed method significantly improves face recogni-tion performance and outperforms state-of-the-art methods in both constrained and unconstrained environments. Published on April 3, 2018 April 3, 2018 • 44 Likes • 3 Comments Wagner et al. Robust nuclear norm regularized regression for face recognition with occlusion Jianjun Qiana, Lei Luoa, Jian Yanga,n, Fanlong Zhanga, Zhouchen Linb a School of Computer Science and Engineering, Nanjing University of Science and Technology, China b Key Laboratory of Machine Perception (MOE), Peking University, China article info Article history: Received 23 September 2014 The algorithm is publicly opened in FaceX-Zoo [3].Besides, we use a face parsing model trained on Lapa [2] dataset to deal with facial occlusion. Remember to remove the key from your code when you're done, and never post it publicly. However, recognizing faces with partial occlusion is still challenging for existing face recognizers, which is heavily desired in real-world applications concerning surveillance and security. Learning Meta Face Recognition in Unseen Domains Jianzhu Guo1,2 Xiangyu Zhu1,2 Chenxu Zhao3 Dong Cao1,2 Zhen Lei1,2∗ Stan Z. Li4 1CBSR & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 3Mininglamp Academy of Sciences, Mininglamp Technology Jian Zhao, Lin Xiong, Yu Cheng, Yi Cheng, Jianshu Li, Li Zhou, Yan Xu, Karlekar Jayashree, Sugiri Pranata, Shengmei Shen, Junliang Xing, Shuicheng Yan, Jiashi Feng. Elhamifar and Vidal [9] proposed a more robust classification method using structured sparse representa-tion, while Gao et al. April 2021 PersonDirectory. 2.1. — Face Detection: A Survey, 2001. GitHub is where people build software. ), with occlusion (e.g. These methods could arguably provide a more robust representation to our problem. Face Recognition is a well researched problem and is widely used in both industry and in academia. Occluded Face Recognition Using Low-rank Regression with Generalized Gradient Direction ... a very effective method to solve the contiguous face occlusion recognition problem is proposed. Experimental results on real world data show that our proposed algorithm can achieve 98.64% accuracy on face detection and 98.56% accuracy on face occlusion detection, even though there are severe occlusions in faces, at a speed of up to 12 frames per second. In this paper, a transfer learning based on … See also a follow-up project which includes all the above as well as mid-level facial details and occlusion handling: Extreme 3D face reconstruction Available also as a docker for easy install. However, it does not work to detect a face when the face has facial occlusion. 26, NO. In order to deal with facial occlusion effectively, the authors propose a powerful but simple face representation method, called adaptive Weberfaces (AdapWeber), based on … "Face Dataset" and other potentially trademarked words, copyrighted images and copyrighted readme contents likely belong to the legal entity who owns the "Jian667" organization. Chimpanzee face recognition from videos in the wild using deep learning D. Schofield, A. Nagrani, A. Zisserman, M. Hayashi, T. Matsuzawa, D. Biro, S. Carvalho ... Face often turned away Bodies prone to heavy occlusion and overlap. The points and the bounding box returned were drawn in the images. CNN Face Detector in Dlib 28 Full PDFs related to this paper. Go to the Azure portal. Once the embeddings are … However, recognizing faces with partial occlusion is still challenging for existing face recognizers which is heavily desired in real-world applications concerning surveillance and security. Face recognition is always challenging issue due to face expression and orientation that never predictable in scene [11-13]. An embedding is a collective name for mapping input features to vectors. I need to develop a face recognition system in using Angular with Azure Face API. Occlusion: Little previous work has been done to quan-tify detection performance in the presence of occlusion (us-ing real data). Face recognition is one of the most important applications in video surveillance and computer vision. In Compressed Sensing: Theory and Applications, 2012 [2] used Hybrid-CNN to infer image sentiment of social events. This is the guildline for face recognition in Azure Face API IEEE International Conference on Face and Gesture Recognition (FG), 2017. Also if any external entity like optical, cap etc are worn while detection might difficult [4, 11] or faulty. Face detection is a necessary first-step in face recognition systems, with the purpose of localizing and extracting the face region from the background. (JCR Q1, CCF B) [ paper] Guangwei Gao, Pu Huang, Dong Yue, Wankou Yang. Several methods have been proposed to resolve this issue. 2018). A short summary of this paper. As a result, we propose a novel and com- Face API is a cognitive service that provides algorithms for detecting, recognizing, and analyzing human faces in images. to escape facial recognition by using masks to occlude their faces. Especially during the COVID-19 coronavirus epidemic, wearing a mask has become an effective means of preventing the virus spread. It has been used for a variety of Locality-Constrained Structral Orthogonal Procrustes Regression for Low-Resolution Face Recognition with Pose Variations. The file picture_mask means face images covered by virtual masks. As described, occluded pedestrians are anno-tated with two BBs that denote the visible and full pedes-trian extent. Face recognition implementation is capable of recognizing faces with occlusion, this includes faces wearing masks. Notice the RECOGNITION_MODEL has set to ‘Recognition01’. Important. Part-5 Post-processing steps. In this paper, we for the rst time, advocate a multi-task deep neural network for jointly learning face recognition and facial at-tribute prediction tasks. Occlusion aware facial expression recognition using cnn with attention mechanism Yong Li, Jiabei Zeng, Shiguang Shan, Xilin Chen IEEE Transactions on Image Processing 28 (5), 2439-2450 (TIP), 2020. If there some occlusion on face (such as sunglasses, mask, scarf), the recognition rate will decrease steeply? We review representative face challenges (Sec.2.1) and methods (Sec.2.2), and existing face Re-ID systems (Sec.2.4) in the literature. There are also tons of great tutorials and GitHub repos for affine transformations. For increasing the efficiency of the results they use high-quality images and increase the number of stages for which the classifier is trained. In face recognition, angle of face with side view image is most critical issue. 4. Then scanning line one by one, at first, search the image part where intensity gradient change fast and check whether the part exists in two facial parts equally. Occluded face detection is a challenging detection task due to the large appearance variations incurred by various real-world occlusions. FaceRecognizer - Face Recognition with OpenCV { FaceRecognizer API { Guide to Face Recognition with OpenCV { Tutorial on Gender Classi cation { Tutorial on Face Recognition in Videos { Tutorial On Saving & Loading a FaceRecognizer By the way you don’t need to copy and paste the code snippets, all code has been pushed into my github repository: Face Benchmark&Dataset Face Benchmark and Dataset¶ Face Recognition¶. resolution, and occlusion, face recognition remains a challenging task. Face Recognition Challenges An overview of representative face challenges and benchmarks are sum- In a facial recognition system, these inputs are images containing a subject’s face, mapped to a numerical vector representation. Since there is also other people asking the same question on the issue of its GitHub page, I tend to think this is a design defects of RetinaFace. S. Technology. Download Full PDF Package. face recognition by generating an intra-class dictionary, which contains all possible class variations. You can login with your Microsoft, LinkedIn, GitHub or Facebook account to get one. component in many face analysis tasks, such as facial attribute inference [1], face verification [2], and face recognition [3]. These problems range from lighting differences, occlusion, alignment, segmentation. Face representation aims to extract a set of discriminative features so that the face … [10] introduced a kernelized version of SRC. 3D face recognition has become a trending research direction in both industry and academia. Elhamifar and Vidal [9] proposed a more robust classification method using structured sparse representa-tion, while Gao et al. Human beings perform face recognition automatically every day and practically with no effort. Preparation The first thing you need is a FaceAPI key which you can get here FaceAPI. Her major research interests include Biometrics, Medical Biometrics, and Computer vision. rent face recognition methods are focusing on 2D images, RGB-D or 3D based face recognition shows more robust-ness against large pose, partial occlusion, and uneven illu-mination variations [5, 6, 3, 9]. Based on our preliminary work [], in this paper, we propose a complete and fully automatic framework to improve face recognition in the presence of partial occlusions.Besides the occlusion detection module (which was introduced in []) which can detect the presence of occlusion in patch-level, we adopted GPM-MRF to detect occlusion in pixel-level to facilitate later recognition. Basically, the related work can be divided into three categories: 2D image-based, multi-modal facial data- based, and 3D geometric feature-based. ... Face detection and recognition library that focuses on speed and ease of use. Download PDF. However, two issues hinder a direct application of deep learning approaches. The Third Facial Micro-Expressions Grand Challenge (MEGC): New Learning Methods for Spotting and Recognition. There has been less attention towards the research on face recognition with partial occlusion. The seemingly unrelated area of image matting has been em-ployed for face segmentation [12], face and gait recognition [13], - juan-csv/face_recognition_occlusion 03/04/2021 ∙ by Baojin Huang, et al. Hi Joe, thanks for the comment (and no worries, your English is very understandable). gradient orientations for illumination and occlusion-robust face recognition has been proposed in [24]. Type Conference paper 3D object recognition and classification. An excellent face detection method should not only be robust for variations in illumination, facial expression and occlusion… 2.In order to defend against these attackers who are compromising the integrity of the FRT systems, the state steps up the game with structured occlusion coding for “robust” face recognition (Wen et al., 2016); or using pre-trained model of full frontal faces to remove LFW database (Huang et al. [10] introduced a kernelized version of SRC. Firstly, contem-porary deep models [6–8] for face recognition are trained with web images or photos from personal albums. errors. Facial expressions of emotion are a major channel in our daily communications, and it has been subject of intense research in recent years. Microsoft Face algorithms enable face attribute detection and face recognition. ), with different orientations (e.g. There are many other interesting use cases of Face Recognition:

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