Face representations in deep convolutional neural networks

View robust pairs Introduction • State-of-the-art face recognition → DCNN models • (Taigman et al., 2014; Schroff et al. 2015; Chen et al., 2016; Sankaranrayanan et al., 2016; Ranjan et al., 2017) • DCNNs designed to model primate visual system (Krizhevsky, et al., 2012) • neural network with multiple layers that convolve and pool image data • representations expand in intermediary layers • highly compressed final representation of image emerges at top layer

[1]  Ha Hong,et al.  Explicit information for category-orthogonal object properties increases along the ventral stream , 2016, Nature Neuroscience.

[2]  Rama Chellappa,et al.  HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Rama Chellappa,et al.  Unconstrained face verification using deep CNN features , 2015, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

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

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  Deep Convolutional Neural Network Features and the Original Image , 2016, ArXiv.

[7]  Carlos D. Castillo,et al.  Triplet probabilistic embedding for face verification and clustering , 2016, 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[8]  Anil K. Jain,et al.  Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[10]  Carlos D. Castillo,et al.  An All-In-One Convolutional Neural Network for Face Analysis , 2016, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[11]  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.