A Deep Learning Paradigm for Automated Face Attendance

In this paper, we propose an end-to-end automatic face attendance system using Convolutional Neural Networks (CNNs). Attendance of a student plays an important role in any academic organization. Manual attendance system is very time consuming and tedious. On the other hand, automatic attendance system through face recognition using CCTV camera can be fast and can reduce the man-power involved in that process. Here, we have pipelined one of the best existing architectures such as: (i) Single Image Super-Resolution Network (SRNet) for image super-resolution, (ii) MTCNN for face detection and (iii) FaceNet for face recognition in order to come up with a novel idea of marking attendance. Due to poor video quality of CCTV camera, it becomes difficult to detect and recognize faces accurately and this may reduce the attendance accuracy. To overcome this limitation, we propose a CNN framework called SRNet which super-resolves a given low resolution (LR) image and also increases the face recognition accuracy. We make use of five different datasets i.e. RAISE and DIV2K for SRNet, VGGface2 for FaceNet, LFW and our own dataset for testing and validation purpose. The proposed face attendance system displays a sheet which consists of a list of absent and present persons and the overall attendance record. Our experimental results show that the proposed approach outperforms other existing face attendance approaches.

[1]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Yu Qiao,et al.  A Discriminative Feature Learning Approach for Deep Face Recognition , 2016, ECCV.

[3]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[4]  Zuying Luo,et al.  University Classroom Attendance Based on Deep Learning , 2017, 2017 10th International Conference on Intelligent Computation Technology and Automation (ICICTA).

[5]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Vincent Dumoulin,et al.  Deconvolution and Checkerboard Artifacts , 2016 .

[8]  Bernhard Schölkopf,et al.  EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[9]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Narendra Ahuja,et al.  Single image super-resolution from transformed self-exemplars , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Xiaogang Wang,et al.  Deep Learning Face Representation by Joint Identification-Verification , 2014, NIPS.

[12]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[13]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[14]  Yu Qiao,et al.  Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks , 2016, IEEE Signal Processing Letters.

[15]  Kyoung Mu Lee,et al.  Deeply-Recursive Convolutional Network for Image Super-Resolution , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Jian Yang,et al.  Image Super-Resolution via Deep Recursive Residual Network , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Shuo Yang,et al.  From Facial Parts Responses to Face Detection: A Deep Learning Approach , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[18]  Imdad A. Rizvi,et al.  Automated attendance system using machine learning approach , 2017, 2017 International Conference on Nascent Technologies in Engineering (ICNTE).

[19]  Kyoung Mu Lee,et al.  Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Gang Hua,et al.  A convolutional neural network cascade for face detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Darko Stefanovic,et al.  FaceTime — Deep learning based face recognition attendance system , 2017, 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY).

[23]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[24]  Xiaoou Tang,et al.  Learning a Deep Convolutional Network for Image Super-Resolution , 2014, ECCV.

[25]  M. V. Raghunadh,et al.  Automated attendance management system based on face recognition algorithms , 2013, 2013 IEEE International Conference on Computational Intelligence and Computing Research.

[26]  Kyoung Mu Lee,et al.  Enhanced Deep Residual Networks for Single Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).