Advanced Techniques for Face Recognition under Challenging Environments

Automatically recognizing faces captured under uncontrolled environments has always been a challenging topic in the past decades. In this work, we investigate cohort score normalization that has been widely used in biometric verification as means to improve the robustness of face recognition under challenging environments. In particular, we introduce cohort score normalization into undersampled face recognition problem. Further, we develop an effective cohort normalization method specifically for the unconstrained face pair matching problem. Extensive experiments conducted on several well known face databases demonstrate the effectiveness of cohort normalization on these challenging scenarios. In addition, to give a proper understanding of cohort behavior, we study the impact of the number and quality of cohort samples on the normalization performance. The experimental results show that bigger cohort set size gives more stable and often better results to a point before the performance saturates. And cohort samples with different quality indeed produce different cohort normalization performance. Recognizing faces gone after alterations is another challenging problem for current face recognition algorithms. Face image alterations can be roughly classified into two categories: unintentional (e.g., geometrics transformations introduced by the acquisition devide) and intentional alterations (e.g., plastic surgery). We study the impact of these alterations on face recognition accuracy. Our results show that state-of-the-art algorithms are able to overcome limited digital alterations but are sensitive to more relevant modifications. Further, we develop two useful descriptors for detecting those alterations which can significantly affect the recognition performance. In the end, we propose to use the Structural Similarity (SSIM) quality map to detect and model variations due to plastic surgeries. Extensive experiments conducted on a plastic surgery face database demonstrate the potential of SSIM map for matching face images after surgeries.

Discharge characteristics and discharge simulation model of long air gaps containing a floating conductor

Long air gaps containing a floating conductor are common insulation types in power grids. During the transmission line live-line work, the process of lineman entering the transmission line air gap constitutes a live-line work combined air gap, which is a typical long air gap containing a floating conductor. This thesis investigates the discharge characteristics, the discharge mechanism and a discharge simulation model of long air gaps containing a floating conductor in order to address the engineering issues in live-line work. The innovative achievements of the thesis are as follows: (1) The effect of the gap distance, the floating electrode structure, the switching impulse wavefront time, the altitude, and the deviation of the floating conductor from the axis on the breakdown voltage was determined. (2) The physical process of the discharges in long air gaps containing a floating conductor was determined. The reason why the discharge characteristics of long air gaps containing a floating electrode with complex geometrics and sharp protrusions and long air gaps with a rod-shaped floating electrode are similar has been studied. The formation mechanism of the lowest breakdown voltage area of a long air gap containing a floating conductor is explained. (3) A simulation discharge model of long air gaps containing a floating conductor was established, which can describe the physical process and predict the breakdown voltage. The model can realize the accurate prediction of the breakdown voltage of typical long air gaps containing a floating conductor and live-line work combined air gaps in transmission lines. The findings of the study can provide theoretical reference and technical support for improving the safety of live-line work.