Issn: 2277-9655 [Soni* et al

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Vinay Rishiwal [1], focuses on developing a face recognition system using an extended PCA algorithm. The proposed algorithm uses the concept of PCA and represents an improved version of PCA to deal with the problem of orientation and lightening conditions present in the original PCA. The preprocessing phase of the proposed algorithm emphasize the efficiency of he algorithm even when number of images per person or the orientation is very different.
Maneesh Upmanyu [2], proposes algorithm which makes no restrictive assumptions on the biometric data and is hence applicable to multiple biometrics. Such a protocol has significant advantages over existing biometric cryptosystems, which use a biometric to secure a secret key, which in turn is used for authentication. Author analyze the security of the protocol under various attack scenarios. Experimental results on four biometric datasets (face, iris, hand geometry, and fingerprint) show that carrying out the authentication in the encrypted domain does not affect the accuracy, while the encryption key acts as an additional layer of security.
Wilman W. W. Zou [3], proposes a novel approach to learn the relationship between the high-resolution image space and the VLR image space for face SR. Based on this new approach, two constraints, namely, new data and discriminative constraints, are designed for good visuality and face recognition applications under the VLR problem, respectively. Experimental results show that the proposed SR algorithm based on relationship learning outperforms the existing algorithms in public face databases.
Yogesh Maniktala [4], Biometrics are automated methods of recognizing a person based on a physiological or behavioral characteristic. Among the features measured are: face, fingerprints, hand geometry, handwriting, iris etc. Biometrics is becoming the foundation of an extensive array of highly secure identification and personal verification solutions. As the required level of security rises, the need for highly secure identification and personal verification is also growing. In this paper, we propose an algorithm for robust face recognition.
B. NAGARJUN SINGH [5], presents and analyzes the performance of Principle Component Analysis (PCA) based technique for face recognition. Author consider recognition of human faces with two facial expressions: single and differential. The images that are captured previously constitute the training set. From these images Eigen faces are calculated. The image that is going to be recognized through our system is mapped to the same Eigen spaces.


The main problem lies for video processing is presented below.

Illumination Problem

Illumination problem happens only when the same image with some conditions. So person need to keep with fix lighting condition, fixed the distance, same facial expression and also have the same view point. It can help to emerge extensively different when lighting condition is extensively different.

Pose Problem

Face recognition with various facial poses that is known as pose problem. If face rotation made very huge changes in face appearance it decreases recognition rate. If any person try to match same image with various facial pose, it show the different result.


Our framework work with less illuminated and different pose characters. To complete the task, there exists 3 components which are presented below.

3 Components of our framework:

1. Face Recognition

2. Fate Tracking

3. Face Detection

Fig. 2 shows the architecture of our framework

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