Thermal Human Face Recognition for Biometric Security System

Thermal Human Face Recognition for Biometric Security System

Ayan Seal (Jadavpur University, India), Debotosh Bhattacharjee (Jadavpur University, India), Mita Nasipuri (Jadavpur University, India) and Dipak Kumar Basu (Jadavpur University, India)
DOI: 10.4018/978-1-4666-4868-5.ch001
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Automatic face recognition has been comprehensively studied for more than four decades, since face recognition of individuals has many applications, particularly in human-machine interaction and security. Although face recognition systems have achieved a significant level of maturity with some realistic achievement, face recognition still remains a challenging problem due to large variation in face images. Face recognition techniques can be generally divided into three categories based on the face image acquisition methodology: methods that work on intensity images, those that deal with video sequences, and those that require other sensory (like 3D sensory or infra-red imagery) data. Researchers are using thermal infrared images for face recognition. Since thermal infrared images have some advantages over 2D images. In this chapter, an overview of some of the well-known techniques of face recognition using thermal infrared faces are discussed, and some of the drawbacks and benefits of each of these methods mentioned therein are discussed. This chapter talks about some of the most recent algorithms developed for this purpose, and tries to give a brief idea of the state of the art of face recognition technology. The authors propose one approach for evaluating the performance of face recognition algorithms using thermal infrared images. They also note the results of several classifiers on a benchmark dataset (Terravic Facial Infrared Database).
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In this modern world, people are worried about the necessity of security, related to the various applications they used in their usual life, from ATMs to attendance maintenance systems. A user-friendly but strongly secured environment is required ubiquitously to protect our privacy and identity without being tampered by any unauthorized means. The traditional security systems generally use password or Personal Identification Number (PIN) and magnetic cards that necessitate the memorization of data to access the system and also bear some drawbacks. It is hard to remember password or PIN and can be stolen or guessed by the intruder; cards, tokens, keys can be lost, forgotten, stolen or duplicated and magnetic cards can be corrupted and unreadable by the card reader device. To overcome these difficulties automated biometric recognition systems can be used as a suitable alternative, which was introduced in earlier centuries. The word ‘Biometric’ is derived from the ancient Greek words “bios” meaning life and “metron” meaning measure (Toth, 2005; Jain et al., 2007). So, the meaning is “life measurement.” Biometric systems use various physical characteristics or behaviors of a person, which is shown in Figure 1 and can be categorized as the name of the part(s) of body involved. Computer vision is one such field with which the visual recognition ability of a person can be achieved in face recognition system, which is comparable to that human by measuring some unique biometric properties or characteristics of a human. The journey was begun with the fingerprint, now several different types of modalities (face, iris, retina, etc) are available for human identification and verification.

Figure 1.

Biometric types

Physical feature is suitable for identity purpose and generally obtained from living human body. Commonly used physical features are fingerprints, facial features, hand geometry, and eye features (iris and retina) etc. The most common used personal traits are signature and voices etc. Nearly all-biometric systems work in the same manner. First, a person is registered into a database using the specified method. Information about a certain characteristic of the human is captured. When the person needs to be identified, the system will take the information about the person again, translates this new information with the algorithm, and then compare the new code with the ones in the database to find out a match and hence, identification. The objective or mission of this chapter is to discuss about different thermal face recognition technique and their categorization based on holistic and feature based approaches. This chapter also includes description of thermal infrared image captured by thermal infrared camera, some critical observations on thermal patterns, benefits and drawbacks of thermal images etc. Comparative study on results of different recent and frequently referred methods along with our own methods also included in this chapter, which will attract to the researchers in the area of computer vision and image processing technology.

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Table of Contents
Rajeev Srivastava, Sanjay K. Singh, K.K. Shukla
Rajeev Srivastava, Sanjay K. Singh, K.K. Shukla
Chapter 1
Ayan Seal, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu
Automatic face recognition has been comprehensively studied for more than four decades, since face recognition of individuals has many applications... Sample PDF
Thermal Human Face Recognition for Biometric Security System
Chapter 2
Shrikant Tiwari, Sanjay Kumar Singh
Identification of newborns at birth is a critical issue for hospitals, birthing centers, and other institutions where multiple births occur. With... Sample PDF
Multimodal Biometrics Recognition for Newborns
Chapter 3
Munaga V. N. K. Prasad, Ilaiah Kavati
Recently, a new biometric technology based on human hand vein patterns has attracted the attention of many researchers. This chapter discusses vein... Sample PDF
Biometric Authentication Based on Hand Vein Pattern
Chapter 4
Punam Bedi, Roli Bansal, Priti Sehgal
This chapter focuses on the role of watermarking techniques in biometric systems. Biometric systems are automated systems of verifying or... Sample PDF
Securing Biometrics Using Watermarking
Chapter 5
Mohamed Saifuddin, Lee Seng Yeong, Seng Kah Phooi, Ang Li-Minn
Computer vision has become very important in recent years. It is no longer restricted to a single camera that is only capable of capturing a single... Sample PDF
Stereo Vision-Based Object Matching, Detection, and Tracking: A Review
Chapter 6
Sung Hyun Kim, Rae-Hong Park, Seungjoon Yang, Hwa-Young Kim
This chapter presents an interpolation method of low-computation for a Region Of Interest (ROI) using multiple low-resolution images of the same... Sample PDF
Feature-Based Affine Motion Estimation for Superresolution of a Region of Interest
Chapter 7
Manish Khare, Rajneesh Kumar Srivastava, Ashish Khare
Many methods for computer vision applications have been developed using wavelet theory. Almost all of them are based on real-valued discrete wavelet... Sample PDF
Daubechies Complex Wavelet-Based Computer Vision Applications
Chapter 8
Artur Miguel Arsenio
One of the main concerns for current multimedia platforms is the provisioning of content that provides a good Quality of Experience to end-users.... Sample PDF
Application of Computer Vision Techniques for Exploiting New Video Coding Mechanisms
Chapter 9
Alok Kumar Singh Kushwaha, Rajeev Srivastava
Human Activity Recognition is an active area of research in computer vision with wide-scale applications in video surveillance, motion analysis... Sample PDF
Recognition of Humans and Their Activities for Video Surveillance
Chapter 10
Saurabh Upadhyay, Shrikant Tiwari, Shalabh Parashar
With the growing innovations and emerging developments in sophisticated video editing technology, it is becoming highly desirable to assure the... Sample PDF
Video Authentication: An Intelligent Approach
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