Title
Poster Session A: Classification of Compressed Human Face Images by using Principle Components
Abstract/Description
This paper describes the novel approach of classifying the humans on the basis of their compressed face images. The compression of the face images is performed using Discrete Wavelet Transform (DWT). While the classification encompass the use of Principal Components Analysis (PCA). Classification technique utilizes PCA in some different way. Only first principal component is used as feature vector out of 92 components (since image size is 112×92), causing a better results of 87.39%. The Euclidean distance is used as distance metric. In the end our results are compared to our previous research of classifying the uncompressed images.
Location
Crystal Ball Room A, Hotel Pearl Continental, Karachi, Pakistan
Session Theme
Poster Session A: Artificial Intelligence [AI-1]
Session Type
Poster Session
Session Chair
Dr. Arshad B. Siddiqui
Start Date
28-8-2005 12:30 PM
End Date
28-8-2005 12:50 PM
iRepository Citation
Riaz, Z., Gilgiti, A., & Ali, Z. (2005). Poster Session A: Classification of Compressed Human Face Images by using Principle Components. International Conference on Information and Communication Technologies. Retrieved from https://ir.iba.edu.pk/icict/2005/2005/13
COinS
Poster Session A: Classification of Compressed Human Face Images by using Principle Components
Crystal Ball Room A, Hotel Pearl Continental, Karachi, Pakistan
This paper describes the novel approach of classifying the humans on the basis of their compressed face images. The compression of the face images is performed using Discrete Wavelet Transform (DWT). While the classification encompass the use of Principal Components Analysis (PCA). Classification technique utilizes PCA in some different way. Only first principal component is used as feature vector out of 92 components (since image size is 112×92), causing a better results of 87.39%. The Euclidean distance is used as distance metric. In the end our results are compared to our previous research of classifying the uncompressed images.
