A survey on heterogeneous face recognition: Sketch, infra-red, 3D and low-resolution

Shuxin Ouyang, Timothy Hospedales, Yi-Zhe Song, Xueming Li, Chen Change Loy, Xiaogang Wang
2016 Image and Vision Computing  
Heterogeneous face recognition (HFR) refers to matching face imagery across different domains. It has received much interest from the research community as a result of its profound implications in law enforcement. A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets being established in recent years. This survey provides a comprehensive review of established techniques and recent developments in HFR. Moreover, we offer a detailed account of
more » ... and benchmarks commonly used for evaluation. We finish by assessing the state of the field and discussing promising directions for future research. Relative Geometry [6] Global Holistic Whole image [7, 8, 9] Whole image [10, 11, 12] Whole image [13, 14, 15] Whole image [16, 17, 18] Whole image [19, 20, 21] Whole image [17, 22, 23] Whole image with Deep Encoder [24] Global Patch Regular grid of patches [25, 26] Regular grid of patches [27, 12] Regular grid of patches [28] Regular grid of patches [29, 30] Regular grid of patches [31, 32, 33] Facial Component Active Shape Model Detection [34] Rectangular patches [35] Cross domain Feature-based LBP [32, 34] Eigenface [51] S 2 R 2 [18] Matching 31, 8, 33, 4, 9] NN [27, 52, 19, 20, 21 ] NN [16] NN with χ 2 [29, 30] NN with χ 2 [35, 41] NN with χ 2 [42] NN with HI [34] NN with Cosine [10] NN with Cosine [28] Multi-class (Tr) Bayesian [8], Metric learning [32] Metric learning [53] SVM [48] Verification (Tr) SVM [54, 24] Similarity thresh. (Cosine) [11] SVM[55] Log. Reg. [54], ANN [24] Gentleboost [56, 57]
doi:10.1016/j.imavis.2016.09.001 fatcat:hy666szkk5bgfoazyxgwy6hli4