Section 2 Re-Identification

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rning.com/chap3.html

Chapter 4

Abstract

Deep-Facial Feature-Based Person

Authentication in Surveillance

Sangeetha Asokan and Saravana Sri Subramanian

Yogameena Balasubramanian, Nagavani Chandrasekaran,

Person reidentification (Re-ID) has been a problem recently faced in computer vision. Most of the existing methods focus on body features which are captured in the scene with high-end surveillance system. However, it is unhelpful for authentication. The technology came up empty in surveillance scenario such as in London's subway bomb blast, and Bangalore ATM brutal attack cases, even though the suspected images exist in official databases. Hence, the prime objective of this chapter is to develop an efficient facial feature-based person reidentification framework for controlled scenario to authenticate a person. Initially, faces are detected by faster region-based convolutional neural network (Faster R-CNN). Subsequently, landmark points are obtained using supervised descent method (SDM) algorithm, and the face is recognized, by the joint Bayesian model. Each image is given an ID in the training database. Based on their similarity with the query image, it is ranked with the Re-ID index. The proposed framework overcomes the challenges such as pose variations, low resolution, and partial occlusions (mask and goggles). The experimental results (accuracy) on benchmark dataset demonstrate the effectiveness of the proposed method which is inferred from the observation of receiver operating characteristic (ROC) curve and

Reidentification for

cumulative matching characteristics (CMC) curve.

reidentification, Faster R-CNN, SDM

1. Introduction

65

Keywords: video surveillance, person reidentification, facial feature-based

analysis of such huge video data can improve the quality of surveillance by processing the video faster. Above all, it is more useful for high-level surveillance tasks like suspicious activity detection or undesirable event prediction for timely

Nowadays, a large network of cameras is predominantly used in public places like airports, railway stations, bus stands, and office buildings. These networks of cameras provide enormous video data, which are monitored manually and may be utilized only when the need arises to ascertain the fact. Fascinatingly, an automated

Applications

## Chapter 4
