Accelerated face detector training using the PSL framework

We train a face detection system using the PSL framework [1] which combines the AdaBoost learning algorithm and Haar-like features. We demonstrate the ability of this framework to overcome some of the challenges inherent in training classifiers that are structured in cascades of boosted ensembles (CoBE). The PSL classifiers are compared to the Viola-Jones type cas- caded classifiers. We establish the ability of the PSL framework to produce classifiers in a complex domain in significantly reduced time frame. They also comprise of fewer boosted en- sembles albeit at a price of increased false detection rates on our test dataset. We also report on results from a more diverse number of experiments carried out on the PSL framework in order to shed more insight into the effects of variations in its adjustable training parameters

Image Classification by Multi-Class Boosting of Visual and Infrared Fusion with Applications to Object Pose Recognition

This paper proposes a novel method for multiview object pose classification through sequential learning and sensor fusion. The basic idea is to use images observed in visual and infrared (IR) bands, with the same sampling weight under a multi-class boosting framework. The main contribution of this paper is a multi-class AdaBoost classification framework where visual and infrared information interactively complement each other. This is achieved by learning hypothesis for visual and infrared bands independently and then fusing the optimized hypothesis subensembles. Experiments are conducted on several image datasets including a set of visual and thermal IR images containing 4844 face images in 5 different poses. Results have shown significant increase in classification rate as compared with an existing multi-class AdaBoost algorithm SAMME trained on visual or infrared images alone, as well as a simple baseline classification-fusion algorithm

Real-time visual detection of vehicles and pedestrians with new efficient adaBoost features

International audienceThis paper deals with real-time visual detection, by mono-camera, of objects categories such as cars and pedestrians. We report on improvements that can be obtained for this task, in complex applications such as advanced driving assistance systems, by using new visual features as adaBoost weak classifiers. These new features, the “connected controlpoints” have recently been shown to give very good results on real-time visual rear car detection. We here report on results obtained by applying these new features to a public lateral car images dataset, and a public pedestrian images database. We show that our new features consistently outperform previously published results on these databases, while still operating fast enough for real-time pedestrians and vehicles detection

Multi-view Face Pose Classification by Boosting with Weak Hypothesis Fusion Using Visual and Infrared Images

This paper proposes a novel method for multi-view face pose classification through sequential learning and sensor fusion. The basic idea is to use face images observed in visual and thermal infrared (IR) bands, with the same sampling weight in a multi-class boosting structure. The main contribution of this paper is a multi-class AdaBoost classification framework where information obtained from visual and infrared bands interactively complement each other. This is achieved by learning weak hypothesis for visual and IR band independently and then fusing the optimized hypothesis sub-ensembles. In addition, an effective feature descriptor is introduced to thermal IR images. Experiments are conducted on a visual and thermal IR image dataset containing 4844 face images in 5 different poses. Results have shown significant increase in classification rate as compared with an existing multi-class AdaBoost algorithm SAMME trained on visual or infrared images alone, as well as a simple baseline classification-fusion algorithm

An Automatic Image Capturing System Applied to Identification Photo Booth

[[abstract]]Common automatic capturing systems employ text and voice instructions to guide users to capture their identification (ID) photos, however, the capturing results may not conform to the specifications of ID photo. To address this issue, this study proposes an ID photo capturing algorithm that can automatically detect facial contours and adjust the size of capturing images. In the experiments, subjects were seated at various distance and heights for testing the performance of the proposed algorithm. The experimental results show that the proposed algorithm can effectively and accurately capture ID photos that satisfy the required specifications.[[notice]]補正完

Wide-range head pose estimation for low resolution video

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, February 2008.Includes bibliographical references (p. 85-87).This thesis focuses on data mining technologies to extract head pose information from low resolution video recordings. Head pose, as an approximation of gaze direction, is a key indicator of human behavior and interaction. Extracting head pose information from video recordings is a labor intensive endeavor that severely limits the feasibility of using large video corpora to perform tasks that require analysis of human behavior. HeadLock is a novel head pose annotation and tracking tool. Pose annotation is formulated as a semiautomatic process in which a human annotator is aided by computationally generated head pose estimates, significantly reducing the human effort required to accurately annotate video recordings. HeadLock has been designed to perform head pose tracking on video from overhead, wide-angle cameras. The head pose estimation system used by HeadLock can perform pose estimation to arbitrary precision on images that reveal only the top or back of a head. This system takes a 3D model-based approach in which heads are modeled as 3D surfaces covered with localized features. The set of features used can be reliably extracted from both hair and skin regions at any resolution, providing better performance for images that may contain small facial regions and no discernible facial features. HeadLock is evaluated on video recorded for the Human Speechome Project (HSP), a research initiative to study human language development by analyzing longitudinal audio-video recordings of a developing child. Results indicate that HeadLock may enable annotation of head pose at ten times the speed of a manual approach. In addition to head tracking, this thesis describes the data collection and data management systems that have been developed for HSP, providing a comprehensive example of how very large corpora of video recordings may be used to research human development, health and behavior.by Philip DeCamp.S.M