Improvement of Haar Feature Based Face Detection in OpenCV Incorporating Human Skin Color Characteristic

Abstract

Open Access Research article detection method detects facial features and ignores anything else, such as building, trees and also bodies. However, many face detection method fail to detect correct faces from images. David, Kriegman, and Ahuja presented a survey of face detection and presented the trends of researches in face detection [4]. In the survey authors categorized and evaluated different face detection algorithms. Some limitations of those algorithms were also discussed in a brief. A common problem of the existing methods is that they treat non facial area as a facial area. The popular Haar like feature based face detection [9, 12] also suffers from the same problem. This paper presents a technique to improve feature based face detection introducing human skin color (HSC) characteristic. A number studies are available on human skin color based face detections [5-8, 13-25]. The methods analyzed different color spaces (e.g., RGB, YCbCr, HIS, TSL, HSV) and their main focus was to generate a rule with the help of these color spaces which can determine whether a color is similar to human skin color or not. Different studies have also shown different techniques to model human skin color. In this study, HSC property has been incorporated with the popular Haar Feature Based Face Detection (HFFD) in OpenCV, and found to improve its performance. The rest of the paper is organized as follows. Section II explains HAAR feature based face detection method. Section III presents the proposed face detection technique incorporating of human skin color analysis in HAAR. Section IV presents experimental results to identify the proficiency of the proposed method. Section V concludes the paper with a brief summary. Haar Feature Based Face Detection in OpenCV OpenCV is a very popular tool for object detection. Any types of objects including human faces can be detected by it. Currently OpenCV is using Haar feature based cascaded classifier for face detection [10]. At first the classifier is trained with a lot of positive images (the images containing particular object like car or face we are interested to detect) scaled to same size say 20x20 resolution. And then the classifier is trained with some negative images (arbitrary images that does not contain that particular object like car or face) of same size. After completion of the training process the classifier capture frequently happening Abstract Face detection from a digital image or video stream is used often for various purposes. But sometimes a system detects an object or area as a face where there is no face at all. This paper presents a technique to reduce such wrong detection rate introducing human skin color (HSC) characteristic. The general property of human skin in RGB color space is that it possess R>G>B (i.e., red values are higher than green value and green value is higher than blue). In this study, such HSC property has been incorporated with the popular Haar Feature Based Face Detection (HFFD) in OpenCV, to reduce wrong detection of faces. Proposed HFFD with HSC (HFFD-HSC) has been tested and compared with standard HFFD rigorously on large number images with single and multiple faces. Experimental results identified the effectiveness of HSC incorporation in HFFD to improve its performance reducing wrong detection of faces.