Face Detection In Color Images Research Articles

The application of an interactively recurrent self-evolving fuzzy CMAC classifier on face detection in color images

This study proposes an interactively recurrent self-evolving fuzzy cerebellar model articulation controller (IRSFCMAC) classifier to solve face detection problems. The learning methods of the proposed classifier are based on simultaneous structure and parameter learning. The structure learning is used to decide the proper input space partition, while the parameter learning is based on gradient descent method. The online structure learning does not need to set any initial structure in advance. In other words, the online structure learning algorithm enables the network along of the problem to efficiently identify the required network structure. The advantages of our proposed IRSFCMAC classifier include (1) using a non-constant differentiable Gaussian basis function to model the hypercube structure; (2) applying an interactively recurrent structure to serve as external loops and internal feedbacks by feeding the hypercube cell (rule) firing strength to itself and other hypercube cells (rules); and (3) requiring fewer computing memory. Finally, experimental results show that the proposed IRSFCMAC classifier is a more adaptive and effective face detection than the other classifiers.

A Face Detection Method Used for Color Images

To the problem of face detection in color images, a novel method is proposed based on skin color segmentation and geometry features. Firstly, some common color models are analyzed, and a large amount of skin images are used to establish an YCbCr color model for region segmentation. Then, the morphological processing is executed on the binary image, and the facial regions filtering is conducted by adopting some geometry constraints such as Euler number, the ratio of width and height, centroid. Finally, the face region is located and labeled with a rectangle. Experimental results showed that the proposed method can equilibrate the unbalance between good accuracy and real-time performance to some extent, and is effective in face location.

A Hybrid Face Detection Approach in Color Images with Complex Background

Human face detection in colour images has been researched extensively over the past decades. Face detection has several applications in areas, such as security access control, visual surveillance, video conferencing, intelligent human-computer interfaces and content-based information retrieval. An ideal face detection system should detect faces from a given image/ video regardless of their poses, illumination, scale, age, race, image quality, and image complexity with optimum speed and very low false-positive rate. In this paper a hybrid approach, based on the skin color information and Adaboost-based face detection, is proposed. The key points of the proposed framework are background elimination and down-sizing using the adaptive skin colour classification and segmentation, as well as sub-window size estimation. Therefore, the skin colour segments, as face candidates, were searched instead of the whole image. Meanwhile, the Viola-Jones Adaboost-based face detector was adopted in this research as the final face detector. In order to test the accuracy of the proposed algorithm, the proposed system was implemented and some experiments were also conducted on the standard image datasets such as Caltech (California Institute of Technology) standard image dataset. The proposed hybrid face detection system was compared with the Viola-Jones face detection system. The experiments showed that the proposed approach could efficiently improve the face detection system in both aspects, namely, accuracy (98.88%) and detection time (259.59 ms). Keywords: Color-Based, Face Detection, Illumination, Skin Color Classification

IJIREEICE - Electrical, Electronics, Instrumentation and Control

Human face detection has become a major field of interest in current research because there is no deterministic algorithm to find multiple faces in a given image. In this paper, the performance comparison has been made of two different algorithms for multiple face detection in color images. First algorithm combines HSI and YCbCr color models along with morphological operations. In the second algorithm, RGB color model with Viola-Jones algorithm is used. After making comparison, it is found that; first algorithm gives better detection accuracy (91%) as compared to the second method (88%). But, first algorithm requires more processing time which is matter of concerned in real-time face detection. The average processing time required for first algorithm is about 6.3sec, whereas for second algorithm it is 5.1sec.

Robust Algorithm for Face Detection in Color Images

Robust Algorithm is presented for frontal face detection in color images. Face detection is an important task in facial analysis systems in order to have a priori localized faces in a given image. Applications such as face tracking, facial expression recognition, gesture recognition, etc., for example, have a pre-requisite that a face is already located in the given image or the image sequence. Facial features such as eyes, nose and mouth are automatically detected based on properties of the associated image regions. On detecting a mouth, a nose and two eyes, a face verification step based on Eigen face theory is applied to a normalized search space in the image relative to the distance between the eye feature points. The experiments were carried out on test images taken from the internet and various other randomly selected sources. The algorithm has also been tested in practice with a webcam, giving (near) real-time performance and good extraction results.

Research of Face Detection in Color Image Based on Skin Color

Research of Face Detection in Color Image Based on Skin Color

A new method for face detection in colour images for emotional bio-robots

Emotional bio-robots have become a hot research topic in last two decades. Though there have been some progress in research, design and development of various emotional bio-robots, few of them can be used in practical applications. The study of emotional bio-robots demands multi-disciplinary co-operation. It involves computer science, artificial intelligence, 3D computation, engineering system modelling, analysis and simulation, bionics engineering, automatic control, image processing and pattern recognition etc. Among them, face detection belongs to image processing and pattern recognition. An emotional robot must have the ability to recognize various objects, particularly, it is very important for a bio-robot to be able to recognize human faces from an image. In this paper, a face detection method is proposed for identifying any human faces in colour images using human skin model and eye detection method. Firstly, this method can be used to detect skin regions from the input colour image after normalizing its luminance. Then, all face candidates are identified using an eye detection method. Comparing with existing algorithms, this method only relies on the colour and geometrical data of human face rather than using training datasets. From experimental results, it is shown that this method is effective and fast and it can be applied to the development of an emotional bio-robot with further improvements of its speed and accuracy.

A novel fuzzy rule base system for pose independent faces detection

Human face detection plays an important role in a wide range of applications such as face recognition, surveillance systems, video tracking applications, and image database management. In this paper, a novel fuzzy rule-based system for pose, size, and position independent face detection in color images is proposed. Subtractive clustering method is also applied to decide on the numbers of membership functions. In the proposed system, skin-color, lips position, face shape information and ear texture properties are the key parameters fed to the fuzzy rule-based classifier to extract face candidate in an image. Furthermore, the applied threshold on the face candidates is optimized by genetic algorithm. The proposed system consists of two main stages: the frontal/near frontal face detections and the profile face detection. In the first stage, skin and lips regions are identified in HSI color space, using fuzzy schemes, where the distances of each pixel color to skin-color and lips-color clusters are applied as the input and skin-likelihood and lips-like images are produced as the output. Then, the labeled skin and lips regions are presented to both frontal and profile face detection algorithms. A fuzzy rule-based containing the face and lips position data, along with the lips area and face shape are employed to extract the frontal/near frontal face regions. On the other hand, the profile face detection algorithm uses a geometric moments-based ear texture classification to verify its outcomes. The proposed method is tried on various databases, including HHI, Champion, Caltech, Bao, Essex and IMM databases. It shows about 98, 96 and 90% correct detection rates over 783 samples, in frontal, near frontal and profile face images, respectively.

Using self-organizing fuzzy network with support vector learning for face detection in color images

This paper proposes a three-stage face detection method using self-organizing Takagi–Sugeno (T–S)-type fuzzy network with support vector (SOTFN-SV) learning. SOTFN-SV is a T–S-type fuzzy system constructed by hybridizing fuzzy clustering and support vector machine. The proposed face detection method consists of three stages. In the first stage, SOTFN-SV is applied to skin color segmentation. Color information from the hue and saturation (HS) color space is used. In the second stage, face size and shape filters are employed to exclude some face candidates to reduce the number of false alarms. Shape analysis is based on the fact that an oval face shape can be approximated by an elliptical shape, and a best fitting ellipse to each connected skin region is found for analysis. In the final stage, colors of the eyes, mouth, and face skin regions of the remaining face candidates are used as detection features. An SOTFN-SV color filter uses these features as inputs to make a final detection decision. The proposed method has a fast detection speed and detects not only the face, but also its size and orientation. Experimental results verify the efficiency and effectiveness of the proposed face detection method.

Face detection in color images

Human face detection plays an important role in applications such as video surveillance, human computer interface, face recognition, and face image database management. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds. Based on a novel lighting compensation technique and a nonlinear color transformation, our method detects skin regions over the entire image and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye, mouth, and boundary maps for verifying each face candidate. Experimental results demonstrate successful face detection over a wide range of facial variations in color, position, scale, orientation, 3D pose, and expression in images from several photo collections (both indoors and outdoors).