A Practical Transfer Learning Algorithm for Face Verification

By Xudong Cao, David Paul Wipf, Fang Wen, Genquan Duan. 2013

223 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Faceless Person Recognition: Privacy Implications in Social Media (2016, background): “Like other areas of computer vision, the last years have seen a shift from classifiers based on handcrafted features and metric learning approaches [13, 14, 15, 16, 17, 18, 19] towards methods based on deep learning [20, 21, 22, 23, 24, 25, 26, 27].”
Deep Learning Face Representation by Joint Identification-Verification (2014, methodology): “13 TL Joint Bayesian [2] 96.”
Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not? (2015, background): “Extensive works have been done to push the accuracy limit on it [3, 16, 4, 1, 2, 5, 11, 10, 12, 14, 13, 17, 9].”
Web-scale training for face identification (2014, methodology): “Current state-of-the-art methods for unconstrained face recognition and verification (the task of predicting whether two face images belong to the same person or not) [22, 2, 18] employ a similar protocol: they use a fairly large collection of images to learn a robust representation or metric, and t”
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace (2014, methodology): “33%) [9] VisionLabs (92.”
Deeply learned face representations are sparse, selective, and robust (2014, methodology): “We also compare to the high-dimensional LBP features [7, 5] with various feature dimensions.”
Person Recognition in Personal Photo Collections (2015, background): “Both settings have seen a recent shift from sophisticated classifiers based on hand-crafted features and metric learning approaches [20, 7, 5, 29, 26, 41, 1], towards methods based on deep learning [37, 36, 43, 33, 27, 38, 21].”
Unconstrained Face Recognition: Identifying a Person of Interest From a Media Collection (2014, unknown): “[13]”
Deep Learning Face Representation from Predicting 10,000 Classes (2014, methodology): “[6] proposed a practical transfer learning algorithm to adapt the Joint Bayesian model from the source domain to the target domain.”
Sparsifying Neural Network Connections for Face Recognition (2015, methodology): “Our weight sparsifying algorithm is evaluated on the LFW dataset [17] and the YouTube Faces dataset [33], respectively, which has been extensively evaluated for face recognition in unconstrained conditions in recent years [5, 10, 23, 8, 30, 27, 26, 28, 31, 22].”
Recover Canonical-View Faces in the Wild with Deep Neural Networks (2014, result): “The FR+FCN (whole face+components) achieves the accuracy of 96 .4 5 percent , which performs slightly better than the best results [9] and improves 4 percent compared to [30].”
Person Recognition in Personal Photo Collections (2017, background): “and metric learning approaches [14], [15], [16], [17].”
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition (2014, background): “, [26], [28], [29], [37], [38], MDML-DCPs also benefits from recent progress in face alignment algorithms [24], [25] that accurately locate dense facial feature points in real-time.”
DeepFace: Closing the Gap to Human-Level Performance in Face Verification (2014, methodology): “tem that uses a large data set of labeled faces [5] employs a clever transfer learning technique which adapts a Joint Bayesian model [6] learned on a dataset containing 99,773 images from 2,995 different subjects to the LFW image domain.”
Unconstrained face verification using deep CNN features (2015, methodology): “To utilize the positive and negative label information available from the training dataset, we learn a joint Bayesian metric which has achieved good performances on face verification problems [5][4].”
DeepID3: Face Recognition with Very Deep Neural Networks (2015, unknown): “13 TL Joint Bayesian [2] 96 .”