Informatics Publishing Limited

J. S. Jayasudha ¹

Affiliations
1 Sree Chitra Thirunal College of Engineering, Trivandrum, IN

Abstract

Exponential growth of Internet results in increased network bandwidth usage. Since it is expensive to increase the bandwidth capacity of the network, alternate software technologies are developed. Web caching and pre-fetching techniques are recognized as the most important techniques for web latency reduction. A user requesting content served by the cache is able to avoid the delays inherent in the web such as slow servers and congested networks. Pre-fetching techniques are used for fetching the anticipated web pages earlier. Pre-fetching technique has been incorporated in web cache cluster for reducing latency and accessing web pages without much bandwidth consumption.

Keywords

Bandwidth, Congestion, Latency, Web Server.

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A. Thamizharasi ¹, J. S. Jayasudha ²

Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Science and Engineering, Sree Chitra Thirunal College of Engineering, IN

Abstract

Face recognition system is gaining more importance in social networks and surveillance. The face recognition task is complex due to the variations in illumination, expression, occlusion, aging and pose. The illumination variations in image are due to changes in lighting conditions, poor illumination, low contrast or increased brightness. The variations in illumination adversely affect the quality of image and recognition accuracy. The illumination variations in face image have to be pre-processed prior to face recognition. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is an image enhancement technique popular in enhancing medical images. The proposed work is to create illumination invariant face recognition system by enhancing Contrast Limited Adaptive Histogram Equalization technique. This method is termed as "Enhanced CLAHE". The efficiency of Enhanced CLAHE is tested using Fuzzy K Nearest Neighbour classifier and fisher face subspace projection method. The face recognition accuracy percentage rate, Equal Error Rate and False Acceptance Rate at 1% are calculated. The performance of CLAHE and Enhanced CLAHE methods is compared. The efficiency of the Enhanced CLAHE method is tested with three public face databases AR, Yale and ORL. The Enhanced CLAHE has very high recognition accuracy percentage rate when compared to CLAHE.

Keywords

Illumination Invariant, Face Recognition, Contrast Limited Adaptive Histogram Equalization (CLAHE), Enhanced CLAHE, Fisher Face.

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A. Thamizharasi ¹, J. S. Jayasudha ²

Affiliations
1 Department of Computer Science and Engineering, Mohandas College of Engineering & Technology, IN
2 Department of Computer Science and Engineering, SCT College of Engineering, IN

Abstract

The face recognition is popular in video surveillance, social networks and criminal identifications nowadays. The performance of face recognition would be affected by variations in illumination, pose, aging and partial occlusion of face by Wearing Hats, scarves and glasses etc. The illumination variations are still the challenging problem in face recognition. The aim is to compare the various illumination normalization techniques. The illumination normalization techniques include: Log transformations, Power Law transformations, Histogram equalization, Adaptive histogram equalization, Contrast stretching, Retinex, Multi scale Retinex, Difference of Gaussian, DCT, DCT Normalization, DWT, Gradient face, Self Quotient, Multi scale Self Quotient and Homomorphic filter. The proposed work consists of three steps. First step is to preprocess the face image with the above illumination normalization techniques; second step is to create the train and test database from the preprocessed face images and third step is to recognize the face images using Fuzzy K nearest neighbor classifier. The face recognition accuracy of all preprocessing techniques is compared using the AR face database of color images.

Keywords

Illumination Normalization, Contrast Stretching, Power Law, Homomorphic Filter, Log Transformations, FKNN Classifier.

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A. Thamizharasi ¹, J. S. Jayasudha ²

Affiliations
1 Manonmaniam Sundaranar University, Abhishekapatti, Tirunelveli -627012, Tamilnadu, IN
2 Department of Computer Science and Engineering, SCT College of Engineering, Pappanamcode, Trivandrum, Kerala, IN

Abstract

Automatic face recognition performance is affected due to the head rotations and tilt, lighting intensity and angle, facial expressions, aging and partial occlusion of face using Hats, scarves, glasses etc. In this paper, illumination normalization of face images is done by combining 2D Discrete Cosine Transform and Contrast Limited Adaptive Histogram Equalization. The proposed method selects certain percentage of DCT coefficients and rest is set to 0. Then, inverse DCT is applied which is followed by logarithm transform and CLAHE. Thesesteps create illumination invariant face image, termed as 'DCT CLAHE' image. The fisher face subspace method extracts features from 'DCT CLAHE' imageand features are matched with cosine similarity. The proposed method is tested in AR database and performance measures like recognition rate, Verification rate at 1% FAR and Equal Error Rate are computed. The experimental results shows high recognition rate in AR database.

Keywords

Facerecognition, DCT CLAHE, recognition rate, AR, 2D DCT, CLAHE etc.

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