EFFECTS OF DIFFERENT DISTANCE MEASURES ON ETHNICITY IDENTIFICATION
DOI:
https://doi.org/10.25098/3.2.23Keywords:
Ethnicity Identification, Color Space, Features Extraction, k-Nearest Neighbors, Distance MetricsAbstract
Facial recognition becomes an active research area of computer vision which provides the demographic information such as gender, age, and ethnicity. The feature extraction and classification technique(s) used to recognize facial images play an important role in achieving high identification rate for a recognition system. The present study is a comparative analysis of two feature extraction techniques: Discrete Wavelet Transform and Discrete Cosine Transform with k-Nearest Neighbor classifiers. It uses 9 different distance functions: City Block, Euclidean, Minkowski, Chebyshev, Standard Euclidean, Cosine, Correlation, Spearman, and Mahalanobis to find the similarity measure between face images. The performance is evaluated in term of the identification accuracy rate and identification time. A series of experimentations is performed on 1200 face images are collected from different standard databases. Experimental outcomes demonstrate that for DWT and DCT feature extraction, city block distance and Euclidean distance metrics produce highly accurate identification rate than other distance metrics.
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