Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.
Title: Deep Learning-based Heterogeneous Face Recognition
Abstract: Face Recognition (FR) is one of the most widely studied problems in computer vision and biometrics research communities due to its applications in authentication, surveillance, and security. Various methods have been developed over the last two decades that specifically attempt to address the challenges such as aging, occlusion, disguise, variations in pose, expression, and illumination. In particular, convolutional neural network (CNN) based FR methods have gained significant traction in recent years. Deep CNN-based methods have achieved impressive performances on the current FR benchmarks. Despite the success of CNN-based methods in addressing various challenges in FR, they are fundamentally limited to recognizing face images that are collected near-infrared spectrum. In many practical scenarios such as surveillance in low-light conditions, one has to detect and recognize faces that are captured using thermal cameras. However, the performance of many deep learning-based methods degrades significantly when they are presented with thermal face images.
Thermal-to-visible face verification is a challenging problem due to the large domain discrepancy between the modalities. Existing approaches either attempt to synthesize visible faces from thermal faces or extract robust features from these modalities for cross-modal matching. We present a work in which we use attributes extracted from visible images to synthesize the attribute-preserved visible images from thermal imagery for cross-modal matching. A pre-trained VGG-Face network is used to extract the attributes from the visible image. Then, a novel multi-scale generator is proposed to synthesize the visible image from the thermal image guided by the extracted attributes. Finally, a pre-trained VGG-Face network is leveraged to extract features from the synthesized image and the input visible image for verification.
Rama Chellappa, Department of Electrical and Computer Engineering
Carlos Castillo, Department of Electrical and Computer Engineering
Vishal Patel, Department of Electrical and Computer Engineering