Integrating colour models for more robust feature detection

Pattern Recognition Letters, 2012

Skin detection is used in applications ranging from face detection, tracking body parts and hand gesture analysis, to retrieval and blocking objectionable content. In this paper, we investigate and evaluate (1) the effect of color space transformation on skin detection performance and finding the appropriate color space for skin detection, (2) the role of the illuminance component of a color space, (3) the appropriate pixel based skin color modeling technique and finally, (4) the effect of color constancy algorithms on color based skin classification. The comprehensive color space and skin color modeling evaluation will help in the selection of the best combinations for skin detection. Nine skin modeling approaches (AdaBoost, Bayesian network, J48, Multilayer Perceptron, Naive Bayesian, Random Forest, RBF network, SVM and the histogram approach of Jones and Rehg ) in six color spaces (IHLS, HSI, RGB, normalized RGB, YCbCr and CIELAB) with the presence or absence of the illuminance component are compared and evaluated. Moreover, the impact of five color constancy algorithms on skin detection is reported. Results on a database of 8991 images with manually annotated pixel-level ground truth show that (1) the cylindrical color spaces outperform other color spaces, (2) the absence of the illuminance component decreases performance, (3) the selection of an appropriate skin color modeling approach is important and that the tree based classifiers (Random forest, J48) are well suited to pixel based skin detection. As a best combination, the Random Forest combined with the cylindrical color spaces, while keeping the illumi-Email addresses: rehanmarwat1@gmail.com (Rehanullah Khan), a.hanbury@ir-facility.org (Allan Hanbury), julian@disi.unitn.it (Julian Stöttinger), bais@ieee.org (Abdul Bais) nance component outperforms other combinations, and (4) the usage of color constancy algorithms can improve skin detection performance.

Digital Signal Processing, 2010

Skin colour detection is a technique very used in most of face detectors to find faces in images or videos. However, there is not a common opinion about which colour space is the best choice to do this task. Therefore, the motivation for our study is to discover which colour model is the best option to build an efficient face detector which can be embedded in a functional face recognition system. We have studied 10 of the most common and used colour spaces doing different comparisons among them, in order to know which one is the best option for human skin colour detection. In concrete, we have studied the models: RGB, CMY, YUV, YIQ, YPbPr, YCbCr, YCgCr, YDbDr, HSV-or HSI-and CIE-XYZ. To make the comparison among them, we have used 15 truth images where the skin colour of a face is clearly separated from the rest of the image (background, eyes, lips, hair, etc.). Thus we can compare at level pixel each colour model, doing a detailed study of each format. We present the final conclusions comparing different results, such as: right detections, false positives and false negatives for each colour space. According to the obtained results, the most appropriate colour spaces for skin colour detection are HSV model (the winner in our study), and the models YCgCr and YDbDr. (J.M. Chaves-González), mavega@unex.es (M.A. Vega-Rodríguez), jangomez@unex.es (J.A. Gómez-Pulido), sanperez@unex.es (J.M. Sánchez-Pérez). . He has authored or co-authored 25 ISI journals, and many book chapters and peerreviewed conference proceedings. His current research interest is the application of the high-performance reconfigurable computing to speedup evolutionary algorithms in large optimization problems.

2000 10th Mediterranean Electrotechnical Conference. Information Technology and Electrotechnology for the Mediterranean Countries. Proceedings. MeleCon 2000 (Cat. No.00CH37099)

Face detection is no more unbreakably correlated with face recognition. Instead it has been established as an important tool in the framework of many multimedia applications like indexing, scene classification and news summarization. Inspired of the work presented in [l], many face detection algorithms based on skin color characteristics have appeared in the literature. Most of them face generalization problems due to the skin color model they use. Moreover, their verification stage exclusively depends on simple shape features limiting the reliability of detection. In this work we present a Gaussian model of the skin color distribution whose parameters are re-estimated based on the current image I frame. In this way the generalization problem is limited. Furthermore the verification stage, applied in the detected skin segments, is based on a template matching variation providing a robust detection.

2013

In this paper, an algorithm for segmenting skin regions in color images using color and edge information is presented. Skin colored regions are first detected using a Bayesian model of the human skin color. These regions are further segmented into skin region candidates that satisfy the homogeneity property of the human skin. We show that Bayesian skin color model outperforms many other models such as the piece-wise linear models, Gaussian models and model based on multilayer perceptions. Experimental results indicate that the proposed segmentation algorithm reduces false detection caused by background pixels having skin colors, and more significantly it is capable of separating true skin regions from falsely detected regions.

dcc.ufla.br

Abstract. Skin color detection is often an effective means used to define a set of candidate areas likely to contain faces, hands, or other human organ in a scene. This can be performed by using human skin color models or by threshold of the appropriate color space. A few papers ...

2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

Color description is a challenging task because of large variations in RGB values which occur due to scene accidental events, such as shadows, shading, specularities, illuminant color changes, and changes in viewing geometry. Traditionally, this challenge has been addressed by capturing the variations in physics-based models, and deriving invariants for the undesired variations. The drawback of this approach is that sets of distinguishable colors in the original color space are mapped to the same value in the photometric invariant space. This results in a drop of discriminative power of the color description.

Information, 2017

Color models are widely used in image recognition because they represent significant information. On the other hand, texture analysis techniques have been extensively used for facial feature extraction. In this paper; we extract discriminative features related to facial attributes by utilizing different color models and texture analysis techniques. Specifically, we propose novel methods for texture analysis to improve classification performance of race and gender. The proposed methods for texture analysis are based on Local Binary Pattern and its derivatives. These texture analysis methods are evaluated for six color models (hue, saturation and intensity value (HSV); L*a*b*; RGB; YCbCr; YIQ; YUV) to investigate the effect of each color model. Further, we configure two combinations of color channels to represent color information suitable for gender and race classification of face images. We perform experiments on publicly available face databases. Experimental results show that the proposed approaches are effective for the classification of gender and race.

International Journal of Computer Applications, 2012

Skin is the most widely used primitive in human image processing research and computer vision with application ranging from face detection and person tracking to pornography filtering. It has proven to be a useful and robust cue for detecting human parts in images since (i) it is invariant to orientation and size (ii) it gives extra dimension compared to gray scale methods and (iii) it is fast to process. The main problems with the robustness of skin color detection depend on illumination condition, it varies between individuals, many everyday life objects are skin color like and skin color is not unique. To detect skin tone in images with this complex background, we presented a method based on hybrid approach. In this approach, the Cheddad’s approach is combined with Cr of YCbCr color space. In Cheddad’s approach, the red channel is discarded. Only green and blue channels are used to find the skin tones. Due to the absence of red channel, this approach is not working properly in im...

Recent Advances in Computer Science and Communications, 2021

Introduction: Face Detection is used in many different steams like video conferencing, human-computer interface, face detection, and in the database management of image. Therefore, the aim of our paper is to apply Red Green Blue (RGB) and Hue Saturation Value (HSV) color models in detecting the single, including multiple images of any face. Each color model HSV, Ycbcr and TSL are individually performed with the RGB color model to detect from the single and multiple images, the region of the face. Methods: The morphological operations are performed in the face region to a number of pixels as the proposed parameter to check either an input image contains face region or not. Canny edge detection is also used to show the boundaries of a candidate face region, in the end, the face can be shown detected by using bounding box around the face. Results: The reliability model has also been proposed for detecting the faces in single and multiple images. The results of the experiments reflect t...