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

Training Methods for Shunting Inhibitory Artificial Neural Networks

This project investigates a new class of high-order neural networks called shunting inhibitory artificial neural networks (SIANN\u27s) and their training methods. SIANN\u27s are biologically inspired neural networks whose dynamics are governed by a set of coupled nonlinear differential equations. The interactions among neurons are mediated via a nonlinear mechanism called shunting inhibition, which allows the neurons to operate as adaptive nonlinear filters. The project\u27s main objective is to devise training methods, based on error backpropagation type of algorithms, which would allow SIANNs to be trained to perform feature extraction for classification and nonlinear regression tasks. The training algorithms developed will simplify the task of designing complex, powerful neural networks for applications in pattern recognition, image processing, signal processing, machine vision and control. The five training methods adapted in this project for SIANN\u27s are error-backpropagation based on gradient descent (GD), gradient descent with variable learning rate (GDV), gradient descent with momentum (GDM), gradient descent with direct solution step (GDD) and APOLEX algorithm. SIANN\u27s and these training methods are implemented in MATLAB. Testing on several benchmarks including the parity problems, classification of 2-D patterns, and function approximation shows that SIANN\u27s trained using these methods yield comparable or better performance with multilayer perceptrons (MLP\u27s)

Special Libraries, November 1962

Volume 53, Issue 9https://scholarworks.sjsu.edu/sla_sl_1962/1008/thumbnail.jp

Video classification based on spatial gradient and optical flow descriptors

Feature point detection and local feature extraction are the two critical steps in trajectory-based methods for video classification. This paper proposes to detect trajectories by tracking the spatiotemporal feature points in salient regions instead of the entire frame. This strategy significantly reduces noisy feature points in the background region, and leads to lower computational cost and higher discriminative power of the feature set. Two new spatiotemporal descriptors, namely the STOH and RISTOH are proposed to describe the spatiotemporal characteristics of the moving object. The proposed method for feature point detection and local feature extraction is applied for human action recognition. It is evaluated on three video datasets: KTH, YouTube, and Hollywood2. The results show that the proposed method achieves a higher classification rate, even when it uses only half the number of feature points compared to the dense sampling approach. Moreover, features extracted from the curvature of the motion surface are more discriminative than features extracted from the spatial gradient

Pedestrian sensing using time-of-flight range camera

This paper presents a new approach to detect pedestrians using a time-of-flight range camera, for applications in car safety and assistive navigation of the visually impaired. Using 3-D range images not only enables fast and accurate object segmentation and but also provides useful information such as distances to the pedestrians and their probabilities of collision with the user. In the proposed approach, a 3-D range image is first segmented using a modified local variation algorithm. Three state-of-the-art feature extractors (GIST, SIFT, and HOG) are then used to find shape features for each segmented object. Finally, the SVM is applied to classify objects into pedestrian or non-pedestrian. Evaluated on an image data set acquired using a time-of-flight camera, the proposed approach achieves a classification rate of 95.0%

Pedestrian lane detection for assistive navigation of blind people

Navigating safely in outdoor environments is a challenging activity for vision-impaired people. This paper is a step towards developing an assistive navigation system for the blind. We propose a robust method for detecting the pedestrian marked lanes at traffic junctions. The proposed method includes two stages: regions of interest (ROI) extraction and lane marker verification. The ROI extraction is performed by using colour and intensity information. A probabilistic framework employing multiple geometric cues is then used to verify the extracted ROI. The experimental results have shown that the proposed method is robust under challenging illumination conditions and obtains superior performance compared to the existing methods. © 2012 ICPR Org Committee

Automatic Image Annotation for Semantic Image Retrieval

This paper addresses the challenge of automatic annotation of images for semantic image retrieval. In this research, we aim to identify visual features that are suitable for semantic annotation tasks. We propose an image classification system that combines MPEG-7 visual descriptors and support vector machines. The system is applied to annotate cityscape and landscape images. For this task, our analysis shows that the colour structure and edge histogram descriptors perform best, compared to a wide range of MPEG-7 visual descriptors. On a dataset of 7200 landscape and cityscape images representing real-life varied quality and resolution, the MPEG-7 colour structure descriptor and edge histogram descriptor achieve a classification rate of 82.8% and 84.6%, respectively. By combining these two features, we are able to achieve a classification rate of 89.7%. Our results demonstrate that combining salient features can significantly improve classification of images

Skin color detection for face localization in human-machine communications

This paper presents the proposed user interface design for computers whereby users can navigate in a 3D graphics scene and change camera viewpoint via head movement. This human-machine communication relies very much on the performance of its face localization module, which must determine head pose and track head movement. We have employed the skin color detection approach to face localization. The approach is studied and presented. The experimental results show that our chosen methodology is very effective. Furthermore, we demonstrate that skin color detection approach can cope with the variations of skin color and lighting condition

Learning Pattern Classification Tasks with Imbalanced Data Sets

Non

Pedestrian lane detection in unstructured scenes for assistive navigation

Automatic detection of the pedestrian lane in a scene is an important task in assistive and autonomous navigation. This paper presents a vision-based algorithm for pedestrian lane detection in unstructured scenes, where lanes vary significantly in color, texture, and shape and are not indicated by any painted markers. In the proposed method, a lane appearance model is constructed adaptively from a sample image region, which is identified automatically from the image vanishing point. This paper also introduces a fast and robust vanishing point estimation method based on the color tensor and dominant orientations of color edge pixels. The proposed pedestrian lane detection method is evaluated on a new benchmark dataset that contains images from various indoor and outdoor scenes with different types of unmarked lanes. Experimental results are presented which demonstrate its efficiency and robustness in comparison with several existing methods