Human Face Detection Research Articles

Efficient human face detection in infancy.

Adults detect conspecific faces more efficiently than heterospecific faces; however, the development of this own-species bias (OSB) remains unexplored. We tested whether 6- and 11-month-olds exhibit OSB in their attention to human and animal faces in complex visual displays with high perceptual load (25 images competing for attention). Infants (n = 48) and adults (n = 43) passively viewed arrays containing a face among 24 non-face distractors while we measured their gaze with remote eye tracking. While OSB is typically not observed until about 9 months, we found that, already by 6 months, human faces were more likely to be detected, were detected more quickly (attention capture), and received longer looks (attention holding) than animal faces. These data suggest that 6-month-olds already exhibit OSB in face detection efficiency, consistent with perceptual attunement. This specialization may reflect the biological importance of detecting conspecific faces, a foundational ability for early social interactions.

Security System Design Based on Human Face Detection and Recognition on Android Platform

Security System Design Based on Human Face Detection and Recognition on Android Platform

A Survey of Feature Base Methods for Human Face Detection

The human face is among the most significant objects in an image or video, it contains many important information and specifications, also is required to be the cause of almost all achievable look variants caused by changes in scale, location, orientation, pose, facial expression, lighting conditions and partial occlusions. It plays a key role in face recognition systems and many other face analysis applications. We focus on the feature based approach because it gave great results on detect the human face. Face feature detection techniques can be mainly divided into two kinds of approaches are Feature base and image base approach. Feature base approach tries to extract features and match it against the knowledge of the facial features. This paper gives the idea about challenging problems in the field of human face analysis and as such, as it has achieved a great attention over the last few years because of its many applications in various domains. Furthermore, several existing face detection approaches are analyzed and discussed and attempt to give the issues regarding key technologies of feature base methods, we had gone direct comparisons of the method's performance are made where possible and the advantages/ disadvantages of different approaches are discussed.

IJARCCE - Computer and Communication Engineering

Video Surveillance is important as far as security is concerned these days. Commercial spaces, schools and hospitals, warehouses and other challenging indoor and outdoor environments require high end cameras with PTZ. The current technologies require RFIDs which are costly and hence the security domain in all becomes expensive. This paper describes the use of low cost single -board computer Raspberry Pi which follows face detection algorithm written in Python as a default programming environment. This new technology is less expensive and in this paper it is used as a standalone platform for hosting image processing. The paper aims at developing a system which captures real time images and displays them on browser using TCP/IP. The algorithm for face detection is being implemented on raspberry pi which enables live video streaming along with detection of human faces.

The shape of the face template: Geometric distortions of faces and their detection in natural scenes

Human face detection might be driven by skin-coloured face-shaped templates. To explore this idea, this study compared the detection of faces for which the natural height-to-width ratios were preserved with distorted faces that were stretched vertically or horizontally. The impact of stretching on detection performance was not obvious when faces were equated to their unstretched counterparts in terms of their height or width dimension (Experiment 1). However, stretching impaired detection when the original and distorted faces were matched for their surface area (Experiment 2), and this was found with both vertically and horizontally stretched faces (Experiment 3). This effect was evident in accuracy, response times, and also observers’ eye movements to faces. These findings demonstrate that height-to-width ratios are an important component of the cognitive template for face detection. The results also highlight important differences between face detection and face recognition.

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.

Auto Face Detection and Tracking Algorithm Combining Color and Texture Features Based on Particle Filter

In this paper, an auto human face detection and tracking algorithm combining color and texture features based on particle filter is proposed. Firstly, the proposed approach makes use of AdaBoost algorithm based on skin color to detect the human face, and implements the initial face location. Secondly, the particle filter algorithm based on the weighted color histogram and the histogram of oriented gradients is used to track the human face. Finally, an adaptive window size adjustment strategy can be fused under the particle filter framework to solve the problem of fixed tracking window size. The experimental results show the robustness and accuracy of the algorithm.

A survey on compressed domain video analysis techniques

Image and video analysis requires rich features that can characterize various aspects of visual information. These rich features are typically extracted from the pixel values of the images and videos, which require huge amount of computation and seldom useful for real-time analysis. On the contrary, the compressed domain analysis offers relevant information pertaining to the visual content in the form of transform coefficients, motion vectors, quantization steps, coded block patterns with minimal computational burden. The quantum of work done in compressed domain is relatively much less compared to pixel domain. This paper aims to survey various video analysis efforts published during the last decade across the spectrum of video compression standards. In this survey, we have included only the analysis part, excluding the processing aspect of compressed domain. This analysis spans through various computer vision applications such as moving object segmentation, human action recognition, indexing, retrieval, face detection, video classification and object tracking in compressed videos.

A study of skin color detection using a merging technique

With advances in technology, image-processing techniques are changing daily. All high-tech products are moving toward controls using human touch. Therefore, in the future, human face detection will have an important role in applications such as facial recognition, human computer interfaces, facial image database management, and video surveillance. This study has three parts: (1) a comparison of the effect on skin color of the and threshold values, for three different races’ skin colors; (2) the segmentation of skin and skin-like objects; and (3) the way in which image-merging techniques effectively filter out skin-like interference in a skin-like environment and repair-broken images. This study uses a merging algorithm, which uses a relative Euclidean and 8-neighbors method to merge broken binary images in the region of interest. The experiments show that the completeness of the image is improved, which proves the effectiveness of the method.

An Improved AdaBoost Algorithm

In view of the higher mistaken-detection rate problem of the human face detection in complex conditions, we put forward an improved algorithm. This article Proposes one kind of the method Which unifies the level of difference between the threshold and the feature value of the weak classifier with the weak classifier's overall error rate. Compared to the method which only based on the overall classification error rate to update the weights, this method can achieve higher detection rate while reduces the mistaken-detection rate. This article redefines the training error which is caused When we train the weak classifier, and Proposes MCE-AdaBoost algorithm. The new definition of training error will pay more attention to the error Which erroneously estimates the face Sample as non-face sample; this much more conforms to face detection of this special target detection issue. The experimental results show that MCE-AdaBoost algorithm can effectively improve the detection Performance of the final classifier.

HUMAN FACE DETECTION METHODS USING COMBINED CASCADE OF CLASSIFIERS

The paper presents the improved human face detection method using the combined cascade of classifiers with the improved face candidates’ verification approach, as well as methods and algorithms for the verification level (convolutional neural network) structure generation and training. The combined cascade shows a high detection rate with a very small number of false positives and the proposed candidates’ verification approach is in almost 3 times faster in comparison with the classic verification scheme. The network’s structure generation method allows creating the sparse asymmetric structure of the convolutional neural network automatically. The improved training method uses the adaptive training examples ratio to obtain a trained network with a very low classification error for the positive examples.

Real Time Detection and Tracking of Human Face using Skin Color Segmentation and Region Properties

Real time face detection and face tracking is one of the challenging problem in computer human interaction, video surveillance, biometrics etc. In this paper we are presenting an algorithm for real time face detection and tracking using skin color segmentation and region properties. First segmentation of skin regions from an image is done by using different color models. Skin regions are separated from the image by using thresholding. Then to decide whether these regions contain human face or not we used face features. Our procedure is based on skin color segmentation and human face features (knowledge-based approach). We have used RGB, YCbCr, and HSV color models for skin color segmentation. These color models with thresholds, help to remove non skin like pixel from an image. Each segmented skin regions are tested to know whether region is human face or not, by using human face features based on knowledge of geometrical properties of human face.

Removing the effect light from image and human face detection

Recent years have attention commencement in the most of the facial animation applications therefore the proposed technic of the way to remove the effect of light from the image by a series of steps in accordance with the algorithm and human face detection.In this paper, a new method is displaying the removing effective light for human face detection. It contains two parts: lighting compensation and skin color model. First we offset the high light existing in human face images and removing high light and shadow, second depending on the mechanism of Skin color classification and the morphological segmentation is used to detect face(s) after removing the effect light. This part consists of face detection from during face segmentation and facial feature extraction.

Real Time Detection and Tracking of Human Face Using Skin Color Segmentation and Region Properties

Real Time Detection and Tracking of Human Face Using Skin Color Segmentation and Region Properties

Skin colour correction and faces detection techniques based on HSL and R colour components

Human face detection is the first step towards face recognition, video surveillance and bioinformatics applications. The first step in faces detection is skin colour detection. Skincolour detection in visible spectrum is a very challenging task as the skin colour in an image is sensitive to various factors such as sensor characteristics, illumination, optics and ethnicity. Many researchers emphasise that there is a relationship between surface colours and illumination which is closely related problem of colour constancy. Colour correction stage should come first before applying skin colour detection to get better recognition rate. In this paper, a new approach Illumination Reflection Problem (IRP) has been proposed to correct the skin colour in the image that suffers from illumination reflection problem. IRP approach succeeded to correct the colour of 95% of affected pixels from illumination reflection problem. Also, a new approach to solve colour constancy problem is proposed named MMCrCb (Mean of Medians of Cr Cb). The purpose of this correction approach is to prepare the image prior to applying skin detection technique. Its methodology based on transforming the image to YCbCr then correcting the values of Y, Cb and Cr. After doing correction, transform the image back to RGB. Images from Caltech Image Database that suffer from colour constancy problem were used to test the MMCrCb approach. Also nine skin colour detection techniques were investigated and compared with a new proposed technique for skin detection using HSL and R colour components. The data base used for testing that approach was the MUCT database. The MUCT database provides more diversity of lighting, age and ethnicity. The testing results proved that the proposed skin detection technique using HSL and R is the best among techniques in the literature. In addition to that, the use of MMCrCb colour constancy correction approach with the proposed skin detection technique increases the skin colour pixels classification rate of unbalanced colour images by 20%.

A LabVIEW Design for Frontal and Non-Frontal Human Face Detection System in Complex Background

Biometric identification has advanced vastly since many decades ago. It became a blooming area for research as biometric technology has been used extensively in areas like robotics, surveillance, security and others. Face technology is more preferable due to its reliability and accuracy. By and large, face detection is the first processing stage that is performed before extending to face identification or tracking. The main challenge in face detection is the sensitiveness of the detection to pose, illumination, background and orientation. Thus, it is crucial to design a face detection system that can accommodate those problems. In this paper, a face detection algorithm is developed and designed in LabVIEW that is flexible to adapt changes in background and different face angle. Skin color detection method blending with edge and circle detection is used to improve the accuracy of face detected. The overall system designed in LabVIEW was tested in real time and it achieves accuracy about 97%.

Automatic Face Recognition System using Pattern Recognition Techniques: A Survey

Automatic Face Recognition system is widely applied in new technologies. This system works beyond the ability of human vision. The limited vision of human eye in identifying vast number of human faces is overcome by the automatic face recognition with many more advantages. The basic purpose of face recognition system is to compare the image video which is stored in a database with the image video in real time variation. Many techniques have been used in face recognition system. This paper present a survey of several techniques used in face recognition system, an approach to the detection and identification of human face. Keywordsrecognition, face detection, face extraction.

A Shot Boundary Detection Method for News Video based Human Skin Region (Face) Detection

A New simple approach to detect, classify shot boundaries and store shot boundary frames in Video sequence using human skin region detection based approach is proposed. Human skin region detection is the process of detecting skin region in sequence of frames. Skin region detection is mainly used for the identification of the human face detection. This approach is very much suitable for finding shots in TV News so that we can classify anchor and non-anchor frames to save the overall time which is required to watch overall news,

Recent Progress in Human Face Detection, Tracking and Recognition

Facial recognition is an important research topic in biometrics and has wide applications in pattern recognition and computer vision. This paper aims at providing interested readers with recent progresses in human face detection, tracking, video-based face recognition, and 3D+2D hybrid face recognition. For this purpose, it mainly focuses on those state-of-the-arts methods and technologies that emerged in recent previous few years. Most existing methods in this area are still-image based which do not utilize motion information; whereas most video-based methods work only in 2D video sequences, which are subject to pose and illumination variations. The recent emergence of 3D video cameras capable of producing range image sequences and corresponding texture image sequences simultaneously allows for the possibility of facial recognition in a 3D+2D video-based scenario. In view of this fact, a scheme of face detection, tracking and recognition process in 3D video-based manner is also proposed in this paper with further concerns addressed.