Robust Face Recognition System using AAM and Gabor Feature Vectors

Face recognition is one of the important applications of image processing and it has gained significant attention in wide range of law enforcement areas in which security is of prime concern. Although the existing automated machine recognition systems have certain level of maturity but their accomplishments are limited due to real time challenges. Face recognition systems are impressively sensitive to appearance variations due to lighting, expression and aging. The major metric in modeling the performance of a face recognition system is its accuracy of recognition. This paper proposes a novel method which improves the recognition accuracy as well as avoids face datasets being tampered through image splicing techniques. Proposed method uses a non-statistical procedure which avoids training step for face samples thereby avoiding generalizability problem which is caused due to statistical learning procedure. This proposed method performs well with images with partial occlusion and images with lighting variations as the local patch of the face is divided into several different patches. The performance improvement is shown considerably high in terms of recognition rate and storage space by storing train images in compressed domain and selecting significant features from superset of feature vectors for actual recognition.

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Design of Robust Face Recognition System with Illumination Variation Realized with the Aid of CT Preprocessing Method

Journal of Korean institute of intelligent systems ◽

10.5391/jkiis.2015.25.1.091 ◽

2015 ◽

Vol 25 (1) ◽

pp. 91-96

Author(s):

Yong-Tak Jin ◽

Sung-Kwun Oh ◽

Hyun-Ki Kim

Keyword(s):

Face Recognition ◽

Recognition System ◽

Illumination Variation ◽

Preprocessing Method ◽

Face Recognition System ◽

Robust Face Recognition

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Robust Face Recognition Technique for a Real-Time Embedded Face Recognition System

Pattern Recognition Technologies and Applications ◽

10.4018/978-1-59904-807-9.ch008 ◽

2008 ◽

pp. 188-211

Author(s):

Ting Shan ◽

Abbas Bigdeli ◽

Brian C. Lovell ◽

Shaokang Chen

Keyword(s):

Face Recognition ◽

Embedded System ◽

Principal Component ◽

Recognition System ◽

Active Appearance Models ◽

Face Recognition System ◽

The Face ◽

The Embedded System ◽

Computationally Intensive ◽

Robust Face Recognition

In this chapter, we propose a pose variability compensation technique, which synthesizes realistic frontal face images from nonfrontal views. It is based on modeling the face via active appearance models and estimating the pose through a correlation model. The proposed technique is coupled with adaptive principal component analysis (APCA), which was previously shown to perform well in the presence of both lighting and expression variations. The proposed recognition techniques, though advanced, are not computationally intensive. So they are quite well suited to the embedded system environment. Indeed, the authors have implemented an early prototype of a face recognition module on a mobile camera phone so the camera can be used to identify the person holding the phone.

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