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

Learning-based license plate detection using global and local features

This paper proposes a license plate detection algorithm using both global statistical features and local Haar-like features. Classifiers using global statistical features are constructed firstly through simple learning procedures. Using these classifiers, more than 70% of background area can be excluded from further training or detecting. Then the AdaBoost learning algorithm is used to build up the other classifiers based on selected local Haar-like features. Combining the classifiers using the global features and the local features, we obtain a cascade classifier. The classifiers based on global features decrease the complexity of the system. They are followed by the classifiers based on local Haar-like features, which makes the final classifier invariant to the brightness, color, size and position of license plates. The encouraging detection rate is achieved in the experiments. © 2006 IEEE

Human Facial Detection System

Augmenting human computer interaction with automated analysis and synthesis of facial expressions is the goal towards which much research effort has been devoted to in the last few years. Face recognition and detection is one of the important aspects of natural human machine interfaces; this technology has great applications such as in security systems, capturing image, authentication and in clinical practice. Although humans recognize facial parts virtually without effort or delay, reliable face detection and recognition by a computer system is still a challenging task. The facial part recognition and detection problem is challenging because different individuals have different structure for their nose, eye and ears differently. In this project we are trying to design a face detection and recognition system in real time using the concepts of Haar wavelets, Eigen faces and template matching. In this project we will be using our system for detecting face area, eyes, mouth ears and nose

A fast algorithm for license plate detection in various conditions

This paper proposes a fast algorithm detecting license plates in various conditions. There are three main contributions in this paper. The first contribution is that we define a new vertical edge map, with which the license plate detection algorithm is extremely fast. The second contribution is that we construct a cascade classifier which is composed of two kinds of classifiers. The classifiers based on statistical features decrease the complexity of the system. They are followed by the classifiers based on Haar-features, which make it possible to detect license plate in various conditions. Our algorithm is robust to the variance of the illumination, view angle, the position, size and color of the license plates when working in complex environment. The third contribution is that we experimentally analyze the relations of the scaling factor with detection rate and processing time. On the basis of the analysis, we select the optimal scaling factor in our algorithm. In the experiments, both high detection rate (with low false positive rate) and high speed are achieved when the algorithm is used to detect license plates in various complex conditions. © 2006 IEEE

DETEKSI CITRA WAJAH DENGAN METODE HAAR FEATURE SELECTION

Banyak sistem biometrik yang dapat diterapkan pada proses verifikasi citra, tapi kebanyakan menggunakan teknik autentifikasi yang sama. Proses untuk pendeteksian citra wajah manusia dapat dilakukan secara digital dengan menggunakan komputer dan merupakan salah satu domain dalam aplikasi computer vision. Salah satu metode yang digunakan adalah metode haar feature selection yang merupakan bagian dari metote Viola-Jones. Penelitian ini bertujuan mengetahui  seberapa jauh metode haar feature selection tersebut dapat digunakan untuk deteksi wajah. Metode yang digunakan adalah eksperimen dengan mengekstraksi ciri pada wajah manusia. Hasil yang diperoleh dari ujicoba yang dilakukan terbukti bahwa metode Haar Feature selection dapat digunakan untuk mendeteksi citra wajah dengan akurasi 91,34% dalam waktu 0,61 detik dengan pada jarak 55 cm dari kamera dengan ukuran 120x190 piksel

Design Of Human Facial Feature Recognition System

Augmenting human computer interaction with automated analysis and synthesis of facial expressions is the goal towards which much research effort has been devoted to in the last few years. Facial feature recognition is one of the important aspects of natural human-machine interfaces; it has great applications such as in behavioral science, security systems and in clinical practice. Although humans recognize facial expressions virtually without effort or delay, reliable expression recognition by machine is still a challenging task. The face expression recognition problem is challenging because different individuals display the same expression differently. In this project we are trying to design a facial feature recognition system in real time using the concepts of Haar classifiers, contour concepts, template matching and studying some models related to it. We have tried to first extract face region from the video using above mentioned approach and had tried to extract some facial features and locate their position in the image