Hybrid Deep Learning for Face Verification

By Yi Sun, Xiaogang Wang, Xiaoou Tang. 2013

401 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Xiaogang Wang
2. Luc Van Gool
3. Dacheng Tao
4. Xiaoou Tang
5. Chen Change Loy
6. Yu Qiao
7. Yi Yang
8. Ming-Hsuan Yang

Citations in the corpus

• FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age (2019, neutral): “†CelebA [37] CelebFace [54, 55] LFW [23] 200K X X X no”
• Convolutional Neural Network Architectures for Matching Natural Language Sentences (2014, background): “Matching structured objects rarely goes beyond estimating the similarity of objects in the same domain [23, 24, 19], with few exceptions like [2, 18].”
• Deep Learning Face Representation by Joint Identification-Verification (2014, background): “In recent years, a great deal of efforts have been made for face recognition with deep learning [5, 10, 20, 27, 8, 22, 21].”
• 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].”
• Surpassing Human-Level Face Verification Performance on LFW with GaussianFace (2014, methodology): “Similarly, for the methods in the second category, the architectures of deep networks in [62, 50, 63, 51] (for example, the number of layers, the number of nodes in each layer, etc.”
• Deeply learned face representations are sparse, selective, and robust (2014, background): “Face recognition achieved great progress thanks to extensive research effort devoted to this area [31, 33, 6, 24, 37, 27, 25, 23, 38].”
• Syntax-based Deep Matching of Short Texts (2015, background): “Deep Matching Models There are other works on using deep neural networks for the matching task [ Huang et al. , 2013; Bordes et al. , 2014; Sun et al. , 2013; Lu and Li, 2013; Hu et al. , 2014 ] , which build upon given or learned representations of objects.”
• When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition (2015, methodology): “[23] generates a large number of new pairs to train Figure 7.”
• Learning Deep Face Representation (2014, background): “Another direction is to directly learn the similarity score by deep neural networks [23, 24].”
• DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks (2017, neutral): “Because of the power on learning feature representation, deep models have been widely used for object recognition, detection and other vision tasks [1], [6], [7], [19], [20], [21], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47],”
• A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs (2017, methodology): “The idea of pair-filters here is similar to the one proposed by Sun et al. [41] in terms of face verification.”
• Look at Boundary: A Boundary-Aware Face Alignment Algorithm (2018, background): “, face recognition [75], face verification [48, 49] and face frontalisation [21].”
• DeepID3: Face Recognition with Very Deep Neural Networks (2015, methodology): “An early effort of learning deep face representation in a supervised way was to employ face verification as the supervisory signal [12], which required classifying a pair of training images as being the same person or not.”
• Deep Learning Face Representation from Predicting 10,000 Classes (2014, methodology): “Recently, deep models such as ConvNets [24] have been proved effective for extracting high-level visual features [11, 20, 14] and are used for face verification [18, 5, 31, 32, 36].”
• The Multiverse Loss for Robust Transfer Learning (2015, neutral): “0025 5 CelebFaces [27], 88k betaface.”