Cross-Domain Visual Matching via Generalized Similarity Measure and Feature Learning

Abstract

Cross-domain visual data matching is one of the fundamental problems in many real-world vision tasks, e.g., matching persons across ID photos and surveillance videos. Conventional approaches to this problem usually involves two steps: i) projecting samples from different domains into a common space, and ii) computing (dis-)similarity in this space based on a certain distance. In this paper, we present a novel pairwise similarity measure that advances existing models by i) expanding traditional linear projections into affine transformations and ii) fusing affine Mahalanobis distance and Cosine similarity by a data-driven combination. Moreover, we unify our similarity measure with feature representation learning via deep convolutional neural networks. Specifically, we incorporate the similarity measure matrix into the deep architecture, enabling an end-to-end way of model optimization. We extensively evaluate our generalized similarity model in several challenging cross-domain matching tasks: person re-identification under different views and face verification over different modalities (i.e., faces from still images and videos, older and younger faces, and sketch and photo portraits). The experimental results demonstrate superior performance of our model over other state-of-the-art methods.

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

“…To the best of our knowledge, the current work is the first of its kind that uses demographic features with face recognition and a retrieval algorithm to achieve better accuracies. (vi) Through extensive experiments, we have demonstrated the effectiveness of our approach against the existing methods including CARC [45], GSM1 [52], and GSM2 [52] on MORPH II and FERET datasets. The superior performance of our approach can be attributed to the deeply learned asymmetric facial features and demographic re-ranking strategy to recognize and retrieve face images across aging variations.…”

“…We compared the results of the proposed face-recognition approach with existing methods, including the score-space-based fusion approach presented in [10], age-assisted face recognition [38], CARC [45], GSM1 [52], and GSM2 [52]. The score-space-based approach presented in [10] uses facial-asymmetry-based features to recognize face images across aging variations without demographic estimation.…”

“…It is evident that the proposed approach outperformed the existing methods. The superior performance can be attributed to the demographic-estimation-based reranking compared to the linear scan employed in CARC [45], GSM1 [52], and GSM2 [52]. The performance of our approach is compared with close competitors, including CARC [45], and generalized similarity models [52] (GSM1 and GSM2) in Figure 8a,b in terms of mAP for MORPH II and FERET test sets, respectively.…”

“…The results show that the proposed approach gave the highest mAP on all three test sets compared to gender re-ranking and gender-race re-ranking-based retrieval methods. The performance of our approach is compared with close competitors, including CARC [45], and generalized similarity models [52] (GSM1 and GSM2) in Figure 8a,b in terms of mAP for MORPH II and FERET test sets, respectively. It is evident that the proposed approach outperformed the existing methods.…”

“…It is evident that the proposed approach outperformed the existing methods. The superior performance can be attributed to the demographic-estimation-based re-ranking compared to the linear scan employed in CARC [45], GSM1 [52], and GSM2 [52].…”