Cross-Age Identity Difference Analysis Model Based on Image Pairs for Age Invariant Face Verification

摘要

Face recognition (FR) is a widely studied topic in the field of computer vision research. Although promising results are achieved, FR researches still face challenges of age variations. Most existing FR networks conduct classification based on the feature similarity, which may be misled by large intra class difference under age variations. Instead of calculating feature similarity, in this paper, we derive a novel cross-age face verification framework named Cross-Age Identity Difference Analysis (CIDA) model, which analyzes the identity difference between image pairs under age variations. Specifically, our framework includes two cascading networks. Firstly, an Identity Difference Feature Extractor (IDFE) is proposed to extract the difference information between the input image pair, where the identity discriminant features are effectively extracted, while other interference factors such as age, illumination, posture are suppressed. Secondly, the Direct Cross-age Verification Network (DCVN) is proposed to directly decide whether the input image pair is from the same individual. We derive a novel loss function, where the classification loss with larger age difference is assigned larger weights, which urges the classifier to pay attention to the classification process of the samples with large age gap. Besides, the loss of DCVN are integrated with the loss function of IDFE as a feedback of the final classification performance, improving the discriminant power of IDFE. Through synchronous training of the two networks, we can finally achieve end-to-end network architecture. Compared with the existing cross-age face recognition (CAFR) methods, we do not need to consider feature similarity comparison, which provides a new insight for cross-age face recognition task. Extensive experiments have been performed on the benchmark CAFR datasets which verify the effectiveness of our model.