Face Recognition using Histogram
Teena Varma · International Journal for Research in Applied Science and Engineering Technology · 2020
Human face conveys more information about identification, expression, and emotions of a person. In today's world every individual in the society wants to be more secure from unauthorized authentication. In order to provide more security, "Facial Recognition" has come into the picture and lead a most challenging role of detecting the face with more accurate results without any false identities. To increase the efficiency of the face recognition, histogram based facial recognition is chosen, where a face region is fragmented into a number of regions and histogram values are extracted and they are linked together into a single vector. This vector is compared for the similarities between the facial images and provides a most efficient outcome. Technique used in face recognition system is histogram. The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image. This method is similar to that of edge orientation histograms, scaleinvariant feature transform descriptors, and shape contexts, but differs in that it is computed on a dense grid of uniformly spaced cells and uses overlapping local contrast normalization for improved accuracy.