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Budi Nugroho
University Of Pembangunan Nasional Veteran, Jawa Timur
Computer Science & IT Economics, Econometrics & Finance

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Face Recognition of Robust Regression With Pre-processing Technique using CLAHE technique Budi Nugroho
Prosiding International conference on Information Technology and Business (ICITB) 2017: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 3
Publisher : Proceeding International Conference on Information Technology and Business

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Abstract

One of face recognition method that has been developed to overcome the problem of illumination variation is the Robust Regression. This method uses the Histogram Equalization technique in the pre-processing stage, which is used to reduce the effects of illumination factors on face images. The results of previous research show the face recognition performance of the Robust Regression method (which uses Histogram Equalization technique in the pre-processing stage) is very high. In this research, the Contrast-limited Adaptive Histogram Equalizaton (CLAHE) technique will be used in the pre-processing stage to replace the Histogram Equalization technique. The research was conducted to find out how the effect of pre-processing technique changes on face recognition performance. The empirical experiment was conducted using one of the standard face database data i.e the Extended Yale Face Database B. Based on the experimental results, the average accuracy of face recognition where in the pre-processing stage using the CLAHE technique is 98.06%. This result is better than face recognition performance using the Histogram Equalization technique at the pre-processing stage, where the average accuracy of facial recognition is 96.17%. Keywords: Face Recognition, Robust Regression, Contrast-limited Adaptive Histogram Equalizaton, and Extended Yale Database B.
Effect of Number of Face Images based on Illumination Variation in the Training Process on Face Recognition Results Budi Nugroho; Anny Yuniarti; Eva Yulia Puspaningrum
Prosiding International conference on Information Technology and Business (ICITB) 2019: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 5
Publisher : Proceeding International Conference on Information Technology and Business

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Abstract

The research is related to face recognition which is influenced by illumination factor. The method used is the Robust Regression, which has a better performance than many other methods. The empirical experiment, which uses Yale Face Database B Cropped, is conducted to determine the effect of number of face images in the training process on face recognition perfomance. The hypothesis proposed in this research is the greater number of face images will result in higher facial recognition performance. The empirical experiment was conducted on this research to prove the hypothesis. Based on experiments that have been done, in general, the process of data training with many images will result in high performance of face recognition. But, this trend only occurs in images in the similar illumination condition. Illumination variation of face images also have significant impact on face recognition results. The process of training data with images of illumination variations (from several subsets of the face database) results in better face recognition performance than the process of training data with images of similar illumination conditions (from a subset of the face database). By using 19 images from subset 5 of the face database, face recognition accuracy is obtained at 95.11%. Whereas by only using 5 images from several subsets, obtained face recognition accuracy up to 96.10%. Even by using 7 images from several subsets, the accuracy obtained is up to 99.47%.Keywords: Face Recognition Performance, Robust Regression, Data Training
Co-Authors Anny Yuniarti Eva Yulia Puspaningrum