日時：2019年8月2日(金)　14：45～16：15
 
場所：工学部　1号館　143講義室
 
題目： Identity-preserving face processing for better face recognition
 
講演者：Prof. Chia-Wen LIN
 
概要：Face detection/recognition is essential in video surveillance applications. However, the performance of　current face detection and recognition schemes can be　easily significantly degraded for difficult cases in real-world video surveillance applications such as　detecting and recognizing low-resolution faces in the　wild, and recognizing faces with illumination　variations, viewpoint changes, and facial expression　variations. In this lecture, I will show some methods　and their results in addressing the difficult problems　in face recognition for real-world video surveillance　applications, including identity-preserving face　hallucination, face augmentation and normalization.　Our approaches are based on Siamese Generative　Adversarial Networks (SiGANs) to achieve identity-preserving face hallucination and normalization. We　incorporate the reconstruction error and identity　label information in the loss function of SiGAN. By　iteratively optimizing the loss functions of the　generator and discriminator of SiGAN, we cannot only　maximize the visual fidelity between the reconstructed　faces and their ground-truths, but also ensure the　reconstructed information is useful for identity　recognition. Experimental results on large-scale　public face datasets demonstrate the efficacy of the　proposed approaches.