A hybrid technique for face detection in color images

In this paper, a hybrid technique for face detection in color images is presented. The proposed technique combines three analysis models, namely skin detection, automatic eye localization, and appearance-based face/nonface classification. Using a robust histogram-based skin detection model, skin-like pixels are first identified in the RGB color space. Based on this, face bounding-boxes are extracted from the image. On detecting a face bounding-box, approximate positions of the candidate mouth feature points are identified using the redness property of image pixels. A region-based eye localization step, based on the detected mouth feature points, is then applied to face bounding-boxes to locate possible eye feature points in the image. Based on the distance between the detected eye feature points, face/non-face classification is performed over a normalized search area using the Bayesian discriminating feature (BDF) analysis method. Some subjective evaluation results are presented on images taken using digital cameras and a Webcam, representing both indoor and outdoor scenes

Neural Web Based Human Recognition

Face detection is one of the challenging problems in the image processing. A novel face detection system is presented in this paper. The approach relies on skin-based color features xtracted from two dimensional Discrete Cosine Transfer (DCT) and neural networks, which can be used to detect faces by using skin color from DCT coefficient of Cb and Cr feature vectors. This system contains the skin color which is the main feature of faces for detection, and then the skin face candidate is examined by using the neural networks, which learn from the feature of faces to classify whether the original image includes a face or not. The processing is based on normalization and Discrete Cosin Transfer. Finally the classification based on neural networks approach. The experiment results on upright frontal color face images from the internet show an excellent detection rate

The effects of destination image and perceived risk on revisit intention: a study in the south eastern coast of Sabah, Malaysia

This study investigated the effects of destination image and perceived risk on revisit intention in the South Eastern Coast of Sabah, Malaysia. A total of 171 questionnaires were collected from international tourists through a self-administered questionnaire. The result of this study identified that three dimensions of destination image (travel environment, natural attraction, entertainment, and events) had significant effects on revisit intention. However, perceived risk was not important to the tourists’ revisit intention. The findings have implications on the tourism industry, especially for key players such as the tourism board and travel companies. It also serves as a reference to destinations with a similar risk background

Face Detection from Images Using Support Vector Machine

Detection of patterns in images using classifiers is one of the most promising topics of research in the field of computer vision. A large number of practical applications for face detection exist and contemporary work even suggests that any specialized detectors can be approximated by using fast detection classifiers. In this project, I have developed an algorithm which will detect face from the input image with less false detection rate using combined effects of computer vision concepts. This algorithm utilizes the concept of recognizing skin color, detecting edges and extracting different features from face. The result is supported by the statistics obtained from calculating the parameters defining the parts of the face. The project also implements the highly powerful concept of Support Vector Machine that is used for the classification of images into face and non-face class. This classification is based on the training data set and indicators of luminance value, chrominance value, saturation value, elliptical value and nose, eye & mouth map values

Automatic human face detection in color images

Automatic human face detection in digital image has been an active area of research over the past decade. Among its numerous applications, face detection plays a key role in face recognition system for biometric personal identification, face tracking for intelligent human computer interface (HCI), and face segmentation for object-based video coding. Despite significant progress in the field in recent years, detecting human faces in unconstrained and complex images remains a challenging problem in computer vision. An automatic system that possesses a similar capability as the human vision system in detecting faces is still a far-reaching goal. This thesis focuses on the problem of detecting human laces in color images. Although many early face detection algorithms were designed to work on gray-scale Images, strong evidence exists to suggest face detection can be done more efficiently by taking into account color characteristics of the human face. In this thesis, we present a complete and systematic face detection algorithm that combines the strengths of both analytic and holistic approaches to face detection. The algorithm is developed to detect quasi-frontal faces in complex color Images. This face class, which represents typical detection scenarios in most practical applications of face detection, covers a wide range of face poses Including all in-plane rotations and some out-of-plane rotations. The algorithm is organized into a number of cascading stages including skin region segmentation, face candidate selection, and face verification. In each of these stages, various visual cues are utilized to narrow the search space for faces. In this thesis, we present a comprehensive analysis of skin detection using color pixel classification, and the effects of factors such as the color space, color classification algorithm on segmentation performance. We also propose a novel and efficient face candidate selection technique that is based on color-based eye region detection and a geometric face model. This candidate selection technique eliminates the computation-intensive step of window scanning often employed In holistic face detection, and simplifies the task of detecting rotated faces. Besides various heuristic techniques for face candidate verification, we developface/nonface classifiers based on the naive Bayesian model, and investigate three feature extraction schemes, namely intensity, projection on face subspace and edge-based. Techniques for improving face/nonface classification are also proposed, including bootstrapping, classifier combination and using contextual information. On a test set of face and nonface patterns, the combination of three Bayesian classifiers has a correct detection rate of 98.6% at a false positive rate of 10%. Extensive testing results have shown that the proposed face detector achieves good performance in terms of both detection rate and alignment between the detected faces and the true faces. On a test set of 200 images containing 231 faces taken from the ECU face detection database, the proposed face detector has a correct detection rate of 90.04% and makes 10 false detections. We have found that the proposed face detector is more robust In detecting in-plane rotated laces, compared to existing face detectors. +D2

Face detection and clustering for video indexing applications

This paper describes a method for automatically detecting human faces in generic video sequences. We employ an iterative algorithm in order to give a confidence measure for the presence or absence of faces within video shots. Skin colour filtering is carried out on a selected number of frames per video shot, followed by the application of shape and size heuristics. Finally, the remaining candidate regions are normalized and projected into an eigenspace, the reconstruction error being the measure of confidence for presence/absence of face. Following this, the confidence score for the entire video shot is calculated. In order to cluster extracted faces into a set of face classes, we employ an incremental procedure using a PCA-based dissimilarity measure in con-junction with spatio-temporal correlation. Experiments were carried out on a representative broadcast news test corpus