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Face Recognition Research Paper

2839 Words12 Pages

This proposal focuses on facial recognition type of physiological biometric. Face recognition is perhaps the most friendly and acceptable way to conduct human authentication. These facts rely basically on its easy collectability mechanisms and its non-intrusiveness property, e.g., people generally accept this biometric characteristic as a valid authentication method. Face biometric is one of the types of biometric application image analysis. It’s a true challenge to build an automated system which equals human ability to recognize faces.
There are two main aspects of face biometrics as
1. Face detection
2. Face recognition

1.1. FACE DETECTION As the keyword define that it concerns about …show more content…

Facial recognition can be processed from far away so there is no need contact with the subject. Hence so they are unaware at the time of being scanned. The main challenges of face recognition today are: handling head pose rotation in depth and wide or large lighting changes, age progression i.e. person’s appearance changes. Face recognition system needs to be improved even under good conditions.

1.2.1. BASIC FACE RECOGNITION PROCESS For biometric person authentication, system design and assessment methodology form essential part of the entire process. Generally the face recognition system is comprised of four parts: face image acquisition and detection, face image pre-processing, facial feature extraction, and face matching and recognition. Fig3. Face recognition process
1. Face Image Acquisition: Static face images, dynamic face images, face images at different locations, and face images of different expressions…all can be acquired by …show more content…

EIGENFACE
Eigenface is one of the predominantly used algorithms for face recognition. Karhunan-loeve is based on eigenface technique in which principal component analysis is used. This method is used for feature selection and dimension reduction. Recognition with the help of PCA was firstly experimented by turk and pentland and reconstruction was performed by Kirby and sirovich. In this method any face image could be approximately reconstructed by small collection of weights for each face and standard face picture i.e.eigenpicture. The weights here are obtained by projecting face image onto eigenface. Mathematically eigenfaces are principal componants divide face into feature vectors. The feature vector information can be obtained from covariance matrix. The faces are characterized by linear combination of highest eigenvalues.. One of the main advantages is that this method is that eigenface were invented exactly for those purpose what makes system very efficient. This system performs face recognition in real time and also uses this method along with motion cases to segment faces out of images by discouraging squares that are classified as non face images. It is fast, simple, and practical

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