Domain Alignment Embedding Network for Sketch Face Recognition

Cross Task Modality Alignment Network for Sketch Face Recognition

“…For the first two setup, performance of the CTMAN with the CTMAN*, CTMAN-ResNet18, PCA (Turk, 1991), ET(+PCA) (Tang and Wang, 2004), EP(+PCA) (Galea and Farrugia, 2015), LLE(+PCA) (Chang et al, 2004), CBR (Hu et al, 2013), D-RS (Klare and Jain, 2015), CBR+D-RS (Klare and Jain, 2015), LGMS (Galea and Farrugia, 2016), HAOG (Galoogahi and Sim, 2012), VGG-Face (Parkhi et al, 2015), DEEPS (Galea and Farrugia, 2018), Xu's (Xu et al, 2021), DLFace (Peng et al, 2019), SSR (Peng et al, 2021), andDAEN (Guo et al, 2021) methods are reported in Tables 5, 6. The performance of these compared approaches is directly from Galea and Farrugia (2018), Xu et al (2021), Peng et al (2019), Peng et al (2021), andGuo et al (2021). The extended gallery set in Galea and Farrugia (2018) consists of part images of the FEI, MEDS-II, Multi-PIE (Gross et al, 2010), and FRGC v2.0 4 datasets, these images are frontal and have high quality.…”

“…For the fourth setup, the performance of the CTMAN with the SSD (Mittal et al, 2014), Attribute (Mittal et al, 2017), Transfer Learning (Mittal et al, 2015), and DAEN (Guo et al, 2021 methods are reported in Table 8. The performance of these compared approaches are directly from Mittal et al (2015), Mittal et al (2017), andGuo et al (2021). The SSD and Attribute are traditional methods, whereas Transfer Learning and DAEN are deep learning methods.…”

“…By using a 3-D morphable model to synthesize both photos and sketches to augment the training data, Galea and Farrugia (2018) utilized a fine-tuned VGG-Face network and a triplet loss to determine the identity in a query sketch by comparing it to a gallery set. Guo et al (2021) designed a training episode strategy to alleviate the small sample problem and proposed a domain alignment embedding loss to guide the network to learn discriminative features. Recently, few-shot learning has become appealing choice to deal with a small sample problem.…”

“…Due to the privacy protection problems and the time consuming efforts of sketch drawing, amount of the paired sketch-photo data is limited. Inspired by the few shot learning methods (Vinyals et al, 2016;Snell et al, 2017;Jiang et al, 2018;Guo et al, 2021), a meta learning training episode strategy is introduced to incorporate modality information by sampling image pairs and classes from the training set.…”

“…Note that CTMAN is different from other sketch face recognition schemes, such as Domain Alignment Embedding Network (DAEN) (Guo et al, 2021). The main differences between the CTMAN and the DAEN are as follows: (1) CTMAN uses a two-stream network to extract discriminative modality alignment feature, the two-stream network consists of a ResNet50 backbone, the non-local blocks and the generalized mean (GeM) pooling layers.…”