Component based recognition of objects in an office environment

We present a component-based approach for recognizing objects under large pose changes. From a set of training images of a given object we extract a large number of components which are clustered based on the similarity of their image features and their locations within the object image. The cluster centers build an initial set of component templates from which we select a subset for the final recognizer. In experiments we evaluate different sizes and types of components and three standard techniques for component selection. The component classifiers are finally compared to global classifiers on a database of four objects

Face Detection in Still Gray Images

We present a trainable system for detecting frontal and near-frontal views of faces in still gray images using Support Vector Machines (SVMs). We first consider the problem of detecting the whole face pattern by a single SVM classifer. In this context we compare different types of image features, present and evaluate a new method for reducing the number of features and discuss practical issues concerning the parameterization of SVMs and the selection of training data. The second part of the paper describes a component-based method for face detection consisting of a two-level hierarchy of SVM classifers. On the first level, component classifers independently detect components of a face, such as the eyes, the nose, and the mouth. On the second level, a single classifer checks if the geometrical configuration of the detected components in the image matches a geometrical model of a face

People Recognition in Image Sequences by Supervised Learning

We describe a system that learns from examples to recognize people in images taken indoors. Images of people are represented by color-based and shape-based features. Recognition is carried out through combinations of Support Vector Machine classifiers (SVMs). Different types of multiclass strategies based on SVMs are explored and compared to k-Nearest Neighbors classifiers (kNNs). The system works in real time and shows high performance rates for people recognition throughout one day

Feature Selection for Face Detection

We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first step we reduce the dimensionality of the input space by projecting the data into a subset of eigenvectors. The dimension of the subset is determined by a classification criterion based on minimizing a bound on the expected error probability of an SVM. In the second step we select features from the SVM feature space by removing those that have low contributions to the decision function of the SVM

Component-based face detection

The Problem: The problem is to develop a trainable system for face detection which is able to handle faces rotated in depth and partially occluded faces. Motivation: Faces form a class of fairly similar objects. Each face consists of the same components in the same geometrical configuration. This is the main reason for the success of frontal face detection systems. However, the problem of pose invariance is still unsolved. Detecting faces which are rotated in depth remains a challenging task. Previous Work: Most of the previous work dealt with frontal faces. [1] used clustering to generate face and non-face prototypes. For each test pattern they calculate the distances between the pattern and the prototypes. These distances form the input to a multi-layer perceptron which classifies the pattern into a face and non-face class. In [2] they used a Support Vector Machine (SVM) with a 2nd degree polynomial kernel to classify normalized gray value patterns. A system able to deal with rotations in the image plane was proposed by [3]. It consists of two neural networks: the first estimates the orientation of the face, the second recognizes the derotated faces (frontal views). They extended this approach by using multiple networks of identical structure in the classification step [4]. An approach based on the probabilities of the occurrence of small intensity patterns (16x16 pixels) in the image of the whole face (64x64 pixels) is proposed in [5]. In our laboratory we recently developed a component-based technique [6] for detecting frontal and near frontal face