Face Recognition in Unconstrained Environment

Face Recognition in Unconstrained Environment

Santosh Kumar (Indian Institute of Technology Varanasi, India), Ramesh Chand Pandey (Indian Institute of Technology Varanasi, India), Shrikant Tiwari (Indian Institute of Technology Varanasi, India), and Sanjay Kumar Singh (Indian Institute of Technology Varanasi, India)
DOI: 10.4018/978-1-4666-8654-0.ch011
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Research emphasizes in face recognition has shifted towards recognition of human from both still images and videos which are captured in unconstrained imaging environments and without user cooperation. Due to confounding factors of pose, illumination, image quality, and expression, as well as occlusion and low resolution, current face recognition systems deployed in forensic and security applications operate in a semi-automatic manner. This book chapter presents a comprehensive review of face recognition approaches in unconstrained environment. The objective of this book chapter is to address issues, challenges and recent advancement in face recognition algorithms which may help novel researchers to do innovative research in unconstrained environment. Finally, this chapter provides the stepping stone for future research to unveil how biometrics approaches can be deployed in unconstrained face recognition systems.
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2. Challenges In Face Recognition

Over the last few years, face recognition has gained a rapid success with the development of new approaches and techniques. Due to this success, the rate of face recognition has been increased to well above 90% identification accuracy. Despite of all this success, all face recognition techniques usually suffer from common challenges of image visibility. These challenges are lighting conditions variations, skin variations and face angle variations (Phuong &, Quang, 2010). The lighting conditions in which the pictures are taken are not always similar because the variations in time and place. The example of lighting variations could be the pictures taken inside the room and the pictures taken outside. Due to these variations, a same person with similar facial expressions may appear differently in different pictures. As a result, if the person has single image in the database of face recognition, matching could be difficult with the face detection under different lighting conditions (Beymer, 1994). In the face recognition community, several covariates for face recognition have been identified such as pose, expression, illumination and aging and discussed below:

Key Terms in this Chapter

Biometric Profile: Information used to represent an individual or group in an information system.

Biometrics: Biometrics means “life measurement” but the term is usually associated with the use of unique physiological characteristics to identify an individual.

Identification: Process of retrieving identity by one to many (1 to M) matching of an unknown biometric profile against a set of known profiles.

Biometric Traits: Class of biometric characteristics (e.g., face or stripe pattern) used as source for constructing a biometric profile.

Classification: Classification refers to as assigning a physical object or incident into one of a set of predefined categories.

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