Meet the editors

Pier Luigi Mazzeo obtained an MSc in Computer Science from the University of Salento, Lecce, Italy, in 2001. Since then, he has been working on several research topics regarding artificial intelligence and computer vision. Dr. Mazzeo joined the Italian National Research Council of Italy (CNR) as a researcher in 2002. He is currently involved in projects for algorithms for video object tracking, face detection and recognition, facial ex-

pression recognition, deep neural networks, and machine learning. He has authored and co-authored 80 publications, including 10 papers published in international journals and book chapters. He has also co-authored five national and international patents. Dr. Mazzeo acts as a reviewer for several international journals and for some book publishers. Since 2004, he has been regularly invited to take part in the scientific committees of national and international conferences.

Dr. Srinivasan Ramakrishnan has 20 years of teaching experience and one year of industry experience. He is a professor and the head of the Department of Information Technology at Dr. Mahalingam College of Engineering and Technology, Pollachi, India. He is an associate editor for *IEEE Access* and a reviewer for 25 international journals. He is also on the editorial board of seven international journals. Dr. Ramakrishnan is a guest editor for special issues in

three international journals, including *Telecommunication Systems*. He has published 169 papers in international and national journals and conference proceedings. He has also published two books on cryptography and wireless sensor networks for CRC Press, six books on speech processing, pattern recognition, and fuzzy logic for IntechOpen, and a book on computational techniques for Lambert Academic Publishing.

Paolo Spagnolo received a five-year degree in Computer Science Engineering from the University of Salento, Lecce, Italy, in 2002. Since then, he has worked as a researcher at the Italian National Research Council. His research interests are in the field of artificial intelligence and computer vision. He is currently working on deep learning and its application in the field of automatic monitoring and surveillance of wide areas. He has authored and

co-authored more than 100 publications in international journals, book chapters, and conference proceedings. He has also co-authored six national and international patents. He is co-editor of three international books. He has been involved in the organization of several international conferences and workshops. He also acts as a reviewer for several international journals.

Contents

**Section 1**

Applying Deep Learning

Surveillance Applications

*and Saravana Sri Subramanian*

*by Xiying Li and Zhihao Zhou*

Object Re-Identification Based on Deep Learning

Spatial Domain Representation for Face Recognition *by Toshanlal Meenpal, Aarti Goyal and Moumita Mukherjee*

*by Ravi Sahu*

**Section 2**

**Section 3**

**Preface III**

Detection and Tracking **1**

**Chapter 1 3**

**Chapter 2 25** Multi-Person Tracking Based on Faster R-CNN and Deep Appearance Features

**Chapter 3 49**

Re-Identification **63**

**Chapter 4 65**

**Chapter 5 87**

Face Recognition **111**

**Chapter 6 113**

Deep Siamese Networks toward Robust Visual Tracking *by Mustansar Fiaz, Arif Mahmood and Soon Ki Jung*

*by Gulraiz Khan, Zeeshan Tariq and Muhammad Usman Ghani Khan*

Detecting and Counting Small Animal Species Using Drone Imagery by

Deep-Facial Feature-Based Person Reidentification for Authentication in

*by Yogameena Balasubramanian, Nagavani Chandrasekaran, Sangeetha Asokan* 

## Contents


**Chapter 7 137** Extended Binary Gradient Pattern (eBGP): A Micro- and Macrostructure-Based Binary Gradient Pattern for Face Recognition in Video Surveillance Area *by Nuzrul Fahmi Nordin, Samsul Setumin, Abduljalil Radman and Shahrel Azmin Suandi*

## **Chapter 8 159**

Preface

Visual object tracking (VOT) and face recognition (FR) are both essential tasks in computer vision with various real-world applications, including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. VOT and FT have remained active research topics due to both their opportunities and challenges. Significant efforts have been made by the research community in the past few decades, but VOT and FR have amazing

Major difficulties lie in different challenges, such as occlusions, clutter, illumination change, scale variations, low-resolution targets, target deformation, target re-identification, fast motion, motion blur, in-plane and out-of-plane rotations, and

Traditional object tracking algorithms employed hand-crafted features like pixel intensity, color, and Histogram of Oriented Gradients (HOG) to represent the target in the object appearance model. Although hand-crafted features achieve satisfactory performance in constrained environments, they are not robust to severe

Recently, deep learning using a Convolutional Neural Network (CNN) has achieved a significant performance boost to various computer vision applications. VOT and FR have been affected by this popular trend in order to overcome tracking challenges and obtain better performance in respect to hand-crafted features.

This book presents the state-of-the-art and new algorithms, methods, and systems of these research fields by using deep learning. It is organized into nine chapters across three sections. Section I discusses object detection and tracking ideas and algorithms. Section II examines applications based on re-identification challenges.

The editors thank the authors for their high-level contributions and their proactive

**Pier Luigi Mazzeo and Paolo Spagnolo** National Research Council of Italy (CNR),

Department of Information Technology at

**Srinivasan Ramakrishnan**

Lecce

Pollachi, India

Institute of Applied Sciences and Intelligent Systems (ISASI),

Dr. Mahalingam College of Engineering and Technology,

Section III presents applications based on FR research.

collaboration in the realization of this book.

potential still to be explored.

appearance changes.

target tracking in presence of noise.

Matrix Factorization on Complex Domain for Face Recognition *by Viet-Hang Duong, Manh-Quan Bui and Jia-Ching Wang*

## **Chapter 9 175**

Granular Approach for Recognizing Surgically Altered Face Images Using Keypoint Descriptors and Artificial Neural Network *by Archana Harsing Sable and Haricharan A. Dhirbasi*
