Face Detection and Recognition Using Skin Color and AdaBoost Algorithm Combined with Gabor Features and SVM Classifier

This paper proposes a novel technique for detecting faces in color images using AdaBoost algorithm combined with skin color segmentation. First,skin color model in the YCbCr chrominance space is built to segment the non-skin-color pixels from the image. Then, mathematical morphological operators are used to remove noisy regions and fill holes in the skin-color region, so we can extract candidate human face regions. Finally, these face candidates are scanned by cascade classifier based on AdaBoost for more accurate face detection. This system detects human face in different scales, various poses, different expressions, lighting conditions, and orientation. Experimental results show the proposed system obtains competitive results and improves detection performance substantially.

Automatic face detection in colored images is closely related to face recognition systems, as a preliminary critical required step, where it is necessary to search for the precise face location. We propose a reliable approach for skin color segmentation to detect human face in colored images under unconstrained scene conditions that overcoming the sensitivity to the variation in face size, pose, location, lighting conditions, and complex background. Our approach is based on building multi skin color clustering models using HSV color space, multi-level segmentation, and rule-based classifier. We proposed to use four skin color clustering models instead of single skin clustering model, namely: standard-skin model, shadow-skin model, light-skin model, high-red-skin model. We made an independent skin color clustering models by converting 3-D color space to 2-D without losing color information in order to find the classification boundaries for each skin color pattern class in 2-D. Once we find the classification boundaries, we process the input image with the first-level skin-color segmentation to produce four layers; each layer reflecting its skin-color clustering model. Then an iterative rule-based region grow is performed to create one solid region of interest which is presumed to be a face candidate region that will be passed to the second-level segmentation. In this approach we combine pixel-based segmentation and region-based segmentation using the four skin layers. We also propose skin-color correction (skin lighting) at shadow-skin layer to improve detection rate. In the second-level segmentation we use gray scale to segment the face candidate region into the most significant features using thresholding. Next step is to compute the X-Y-reliefs to locate the accurate position of facial features in each face candidate region and match it with our geometrical knowledge in order to classify the face candidate region to a face or non-face region. We present experimental results of our implementation and demonstrate the feasibility of our approach to be general purpose skin color segmentation for face detection problem.

To realize the eye pupil center location in color face image, a new algorithm based on skin color segmentation and radial symmetry transform is proposed. The algorithm makes full use of the pupil and the surrounding region’s gradient variation characteristics and radial symmetry characteristic, which can effectively solve the interference factors such as uneven spot, frame. Step: First, use the skin color model to detect the face region, and then remove the small block of the background with similar skin color by morphological opening operation and area comparison filtering method. Then, use the horizontal integral projection to determine the candidate eye regions. Finally, apply the radial symmetry transform to the candidate eye region to locate pupil center. Experimental results show that the proposed algorithm has good localization effect, and has strong robustness to various disturbances. Introduction In recent years, line of sight tracking technology is an important and active research area of computer vision. It is widely used in human-computer interaction, auxiliary driving vehicles, virtual reality and military fields [1]. An important process in the line of sight tracking system is the extraction and detection of line of sight characteristic parameters. Eyes as the most important visual organ contains a wealth of sight feature parameters such as the pupil center. Therefore, a fast, accurate and effective eye location algorithm is very important. Proposed Method Fig.1 shows the pupil center location algorithm flow. First, detect face region by the skin color model in the YCbCr color space. Then, using gray level features of eye region, the candidate eye region is detected by the horizontal integral gray projection algorithm [2, 3, 4]. Finally, apply the radial symmetry transform to the candidate eye region to locate pupil center. Face Detection Based on Skin Color Segmentation. Skin color is one of the most striking features of color face images, which is relatively concentrative and stable, and not affected by the brightness of the image. Skin color has a strong clustering feature. Use the ellipse skin color model proposed by Hsu[5] based on YCbCr color space, Where the Y represents the brightness information, and Cb and Cr represent the blue chrominance component the red chrominance component respectively. Ellipse skin color model is shown in Eq.1 and Eq.2, Where Cband Cr represent the original blue component and red component in the YCbCr color space respectively, and x and y represent chrominance components after transformation. Bring X, Y into the left of 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering (ICCMCEE 2015) © 2015. The authors Published by Atlantis Press 1040

There is an abnormal increase in the crime rate and also the number of criminals are increasing, this leads towards a great concern about the security issues. Crime preventions and criminal identification are the primary issues before the police personnel, since property and lives protection are the basic concerns of the police but to combat the crime, the availability of police personnel is limited. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. This Real time criminal identification system based on face recognition works with a fully automated facial recognition system. Haar feature-based cascade classifier and OpenCV LBPH (Local Binary Pattern Histograms) Algorithms are used for Face detection and recognition. This system will be able to detect face and recognize face automatically in real time. An accurate location of the face is still a challenging task. This Framework has been widely used by researchers in order to detect the location of faces and objects in a given image. Face detection classifiers are shared by public communities, such as OpenCV. The automatically tagging feature adds a new dimension to sharing pictures among the people who are in the picture and also gives the idea to other people about who the person is in the image. In our project, we have studied and implemented a pretty simple but very effective face detection using CNN algorithm which takes human skin color into account. Our aim, which we believe we have reached, was to develop a system that can be used by police or investigation department to recognize criminal from their faces. The method of face recognition used is fast, robust, reasonably simple and accurate with a relatively simple and easy to understand algorithms and technique. Key Words: Face Detection, MTCNN, Deep Learning, Real-Time Surveillance, Criminal Identification, CNN

This paper proposes a face detection algorithm of combining skin color segmentation and AdaBoost algorithm. The algorithm set up the skin Gaussian model in YCbCr color space using skin color clustering characteristics. Then sort out the region of skin color, use AdaBoost algorithm to train a classifier to detect face in the image. Based on our experiments, the proposed method shows good results with significant improvements of low error detection rate and better detection speed in both simple and complex background.

In this paper, propose a method of face detection based on adaptive skin color and structure model for multi-pose color images in a complex background. First, a face skin color model is built, and an adaptive Gamma correction is used in the skin color segmentation, which improve the skin color model to adapt to different illumination and multi-poses of face. Then a triangular structure model is constructed specific to the face features. And finally use the structure model to locate the face precisely on the basis of face images of skin color. The experimental results show the approach raised on this paper can adapt to the face detection under the conditions of different illumination environment, various poses and complex background, and the algorithm is quick and effective.

In the current scenario face identification and recognition is an important technique in surveillance. The face is a necessary biometric in humans. Therefore face detection plays a major job in computer vision applications. Several face recognition and emotions classification approaches have been presented throughout the last few decades of research to improve the rate of face recognition for thermal pictures. However, in real-time, lighting conditions might change due to several factors, such as the different times of capture, weather, etc. Due to variations in lighting intensity, the performance of the facial expression recognition system is not good. This paper proposed a model for human thermal face detection and expression classification. Four main steps were involved in this research. Initially, the Difference of the Gaussian (DOG) filter is utilized to crop the input thermal images and then normalize the images using the median filter in pre-processing step. Then, Efficient Net is used for extracting features such as shape, location, and occurrences from thermal face images. After that, detect human faces utilized by the YOLOv4 technique to better emotions classification. Finally, classify the emotions on faces by using the DenseNet technique into seven emotions such as happy, sad, disgust, surprise, anger, fear, and neutral. The proposed method outperforms state-of-art techniques for face recognition on thermal pictures, and classifies the expressions, according to experimentations on the RGB-D-T database. The accuracy, precision, recall, and f1-score metrics will be utilized with the database to assess the efficacy of the proposed methodology. The proposed models achieve a high classification accuracy of 95.97% on the RGB-D-T database. Furthermore, the outcomes show good precision for various face recognition tasks.

This paper presents a novel approach for detection and recognition of multi-view faces whose location is unknown and the illumination conditions are varying. The illumination is a big problem in the face detection and recognition aspects. Two completely different methods are proposed for face detection in this paper. Our proposed methods do not use the skin colour information for face detection. The detection of faces is accomplished after cancelling the effect of the various illumination conditions. Because of the independency of the approaches to the face's skin colour, persons with every kind of skin colours are detected even in completely dark environments. Next, the detected faces are recognized. The experiments have shown that we can combine some proposed aspects of feature based methods with eigenface method and get successful results. The illumination dependency and scaling problems of eigenface method have also been solved by a new methodology in this paper. (9 pages)

The performance of face recognition system is greatly affected by the illumination changes. In this article, we propose a method of face illumination compensation based on neural network and wavelet. It sufficiently combines multi-resolution analysis of wavelet and the self-adaptation learning and good spread ability of BP neural network, thus this method carries out the face illumination compensation. The theory and experiments prove that this method solves the problem of illumination compensation efficiently in the face detection and recognition process. It improves the face detection and recognition under different illumination conditions. Moreover, it has good robustness and can be used in a wide range.KeywordsNeural networkWavelet analyzeIllumination compensationFace recognitionFace detection

Illumination, pose variations, disguises, aging effects and expression variations are some of the key factors that affect the performance of face recognition systems. Face recognition systems have always been studied from a recognition perspective. Our emphasis is on deriving a measure of similarity between faces. The similarity measure provides insights into the role each of the above mentioned variations play in affecting the performance of face recognition systems. In the process of computing the similarity measure between faces, we suggest a framework to compensate for pose variations and introduce the notion of 'half-faces' to circumvent the problem of non-uniform illumination. We used the similarity measure to retrieve similar faces from a database containing multiple images of individuals. Moreover, we devised experiments to study the effect age plays in affecting facial similarity. In conclusion, the similarity measure helps in studying the significance facial features play in affecting the performance of face recognition systems.

Although machine learning and artificial intelligence have been widely applied, noise and interference are still major disturbances to degrade the quality of image transmission and processing efficiency in multimedia data transmission. Based on the face detection system utilizing the machine learning algorithms and artificial intelligence, this paper examines, analyzes and compares the performances of the AdaBoost machine learning algorithm and Convolutional Neural Network (CNN)-based algorithm in processing face information, which is disturbed by channel noise and fading effect encountered in the transmission of face image. The face detection system used is based on HAAR feature extraction. The extracted HAAR features are subjected to classification training and learning of the cascade classifier. Then the face detection is performed on the picture information outside the database. Results of computer simulation show that for the image data affected by the fading and AWGN, the face recognition system still marked the positions of the eyes and mouth with high accuracy. However for fading and higher AWGN, using a machine learning algorithm with a convolutional neural network is better than the ADABOOST algorithm. It can be concluded that the machine learning algorithm can effectively reduce the adverse effect of multimedia data transmission without increasing the SNR and use of higher level of modulation scheme.

The face detection based on skin color features is greatly affected by illumination and the face detection based on AdaBoost algorithm is greatly affected by multi-pose of face, and this means that the large face deflection will make the detection less accurate. So we investigated a face detection method that combines the skin color model and the improved AdaBoost algorithm in this paper. This method first judges whether the collected face image needs illumination compensation. The image is converted from RGB color space to YCbCr color space with higher pixel clustering characteristics and distribution rules by whether it is needed or not. Meanwhile, the candidate regions containing face images are carried out through morphological process and shape select after establishing the Gaussian model. Finally, the face detection results are obtained through detecting the candidate regions with face images by improved AdaBoost algorithm. A large number of experimental results show that this new method can effectively reduce the false detection rate and improve the efficiency of face detection.

Face detection is a key step in computer vision applications, such as face recognition and video surveillance. There is a difficult question in automatically segmenting color images into skin color and background regions when using the method of skin color segmentation. The border of skin color region has some background noise patches, especially using edge detection to extract facial organ characteristics, which will lead to some redundant gradient features near the border. In this paper, we introduce a new method based on the histogram to smooth face contour and reduce background noise patches. The performance of our face detector based on the smoothing contour algorithm has been tested on reference test sets, and clearly show some improvements in face detection which is based on knowledge-based methodology.

Fairness has become an important agenda in computer vision and artificial intelligence. Recent studies have shown that many computer vision models and datasets exhibit demographic biases and proposed mitigation strategies. These works attempt to address accuracy disparity, spurious correlations, or unbalanced representations in datasets in tasks such as face recognition, verification and expression and attribute classification. These tasks, however, all require face detection as the first preprocessing step, and surprisingly, there has been little effort in identifying or mitigating biases in face detection. Biased face detectors themselves pose a threat against fair and ethical AI systems, and their biases may be further passed on to subsequent downstream tasks such as face recognition in a computer vision pipeline. This paper therefore investigates the problem of biases in face detection, focusing on accuracy disparity of detectors between demographic groups including gender, age group, and skin tone. We collect perceived demographic attributes on a popular face detection benchmark dataset, WIDER FACE, report skewed demographic distributions, and compare detection performance between groups. In order to mitigate the biases, we apply three mitigation methods that have been introduced in the recent literature and also propose two novel methods. Experimental results show that these methods are effective in reducing demographic biases. We also discuss how the effectiveness varies by demographic attributes, detection easiness, and multiple detectors, which will shed light on this new topic of addressing face detection bias.

Skin color segmentation has proven to be useful in various application, such as face detection, hand gesture analysis, image content filtering, etc. There are some challenges on skin-color detection, such as wide variety of illumination condition and skin-like color objects appear in the background of image. Therefore, a method is required to cope with illumination condition while detecting skin color. In our proposed method, we combine the chrominance channels of three color spaces, namely HSV, YCbCr and Normalized RGB, to produce better rate result for skin color segmentation. The result shows that the proposed method is able to detect skin pixel and get up to 91.05% correct result while being tested in ECU and HGR Dataset. This result is significantly higher than the other single color spaces.