Zahid Riaz

A Model Based Approach for Expressions Invariant Face Recognition

This paper describes an idea of recognizing the human face in the presence of strong facial expressions using model based approach. The features extracted for the face image sequences can be efficiently used for face recognition. The approach follows in 1) modeling an active appearance model (AAM) parameters for the face image, 2) using optical flow based temporal features for facial expression variations estimation, 3) and finally applying classifier for face recognition. The novelty lies not only in generation of appearance models which is obtained by fitting active shape model (ASM) to the face image using objective functions but also using a feature vector which is the combination of shape, texture and temporal parameters that is robust against facial expression variations. Experiments have been performed on Cohn-Kanade facial expression database using 62 subjects of the database with image sequences consisting of more than 4000 images. This achieved successful face recognition rate up to 91.17% using binary decision tree (BDT), 98.6% using Bayesian Networks (BN) with 10-fold cross validation in the presence of six different facial expressions.

Model Based Analysis of Face Images for Facial Feature Extraction

This paper describes a comprehensive approach to extract a common feature set from the image sequences. We use simple features which are easily extracted from a 3D wireframe model and efficiently used for different applications on a benchmark database. Features verstality is experimented on facial expressions recognition, face reognition and gender classification. We experiment different combinations of the features and find reasonable results with a combined features approach which contain structural, textural and temporal variations. The idea follows in fitting a model to human face images and extracting shape and texture information. We parametrize these extracted information from the image sequences using active appearance model (AAM) approach. We further compute temporal parameters using optical flow to consider local feature variations. Finally we combine these parameters to form a feature vector for all the images in our database. These features are then experimented with binary decision tree (BDT) and Bayesian Network (BN) for classification. We evaluated our results on image sequences of Cohn Kanade Facial Expression Database (CKFED). The proposed system produced very promising recognition rates for our applications with same set of features and classifiers. The system is also realtime capable and automatic.

Feature Extraction and Representation for Face Recognition

Over the past two decades several attempts have been made to address the problem of face recognition and a voluminous literature has been produced. Current face recognition systems are able to perform very well in controlled environments e.g. frontal face recognition, where face images are acquired under frontal pose with strict constraints as defined in related face recognition standards. However, in unconstrained situations where a face may be captured in outdoor environments, under arbitrary illumination and large pose variations these systems fail to work. With the current focus of research to deal with these problems, much attention has been devoted in the facial feature extraction stage. Facial feature extraction is the most important step in face recognition. Several studies have been made to answer the questions like what features to use, how to describe them and several feature extraction techniques have been proposed. While many comprehensive literature reviews exist for face recognition a complete reference for different feature extraction techniques and their advantages/disadvantages with regards to a typical face recognition task in unconstrained scenarios is much needed. In this chapter we present a comprehensive review of the most relevant feature extraction techniques used in 2D face recognition and introduce a new feature extraction technique termed as Face-GLOH-signature to be used in face recognition for the first time (Sarfraz and Hellwich, 2008), which has a number of advantages over the commonly used feature descriptions in the context of unconstrained face recognition. The goal of feature extraction is to find a specific representation of the data that can highlight relevant information. This representation can be found by maximizing a criterion or can be a pre-defined representation. Usually, a face image is represented by a high dimensional vector containing pixel values (holistic representation) or a set of vectors where each vector summarizes the underlying content of a local region by using a high level 1

A real time system for model-based interpretation of the dynamics of facial expressions

Our system runs at 10 fps on a 2.0 GHz processor and an image resolution of 640times480 pixels. High quality objective functions that are learned from annotated example images ensure both an accurate and fast computation of the model parameters. Our demonstrator for facial expression estimation has been presented at several events with political audience and on TV. However, the approach of robust face models fitting, forms the basis of various more applications such as gaze detection or gender estimation. The drawback of our approach is that the data base from which the objective function is learned needs to cover all aspects of face properties. If, for instance, the database did not contain images of bearded men the objective function will fail when confronted with such an image. Furthermore, the data base has to be manually annotated. Although no expert knowledge is required, this task requires a considerable amount of time. An online fitting demonstration is available.

Shape invariant recognition of segmented human face images using eigenfaces

This paper describes an efficient approach for face recognition as a two step process: (1) segmenting the face region from an image by using an appearance based model, (2) using eigenfaces for person identification for segmented face region. The efficiency lies not only in generation of appearance models which uses the explicit approach for shape and texture but also the combined use of the aforementioned techniques. The result is an algorithm that is robust against facial expressions variances. Moreover it reduces the amount of texture up to 12% of the image texture instead of considering whole face image. Experiments have been performed on Cohn Kanade facial database using ten subjects for training and seven for testing purposes. This achieved a successful face recognition rate up to 92.85% with and without facial expressions. Face recognition using principal component analysis (PCA) is fast and efficient to use, while the extracted appearance model can be further used for facial recognition and tracking under lighting and pose variations. This combination is simple to model and apply in real time.

Multi-feature fusion in advanced robotics applications

This paper describes a feature extraction technique from human face image sequences using model based approach. We study two different models with our proposed approach towards multifeature extraction. These features are efficiently used for human face information extraction for different applications. The approach follows in fitting a model to face image using robust objective function and extracting textural and temporal features for three major applications naming 1) face recognition, 2) facial expressions recognition and 3) gender classification. For experimentation and comparative study of our multi-features over two models, we use same set of features with two different classifiers generating promising results to explain that extracted features are strong enough to be used for face image analysis. Features goodness has been investigated on Cohn Kanade Facial Expressions Database (CKFED). The proposed multi-features approach is automatic and real time.

Spatio-temporal Facial Features for HRI Scenarios

Human faces are mostly seen in actions conveying various information in our daily life communication. On the other hand, human brains are capable of extracting this essential information in a very short interval of time resulting in a better interaction with others. For example, in interactive scenarios where human beings are assisted by intelligent systems or robots, it is quite useful to extract sufficient information about the interacting person. In this paper we study biometric and soft-biometric traits of the humans by using their face information using a single feature set which is representative of persons identity, gender and facial behavior. This problem is addressed using spatio-temporal multifeatures (STMF) extracted from image sequences using a 3D face model. Further, this feature set provides robustness against varying head poses and facial expressions. Experiments have been performed on laboratory captured images and three benchmark databases under varying poses and facial expressions. The results have been discussed comparatively with different approaches.

Face recognition using wireframe model across facial expressions

This paper describes face recognition across facial expressions variations. We focus on an automatic feature extraction technique which is not only efficient but also accurate for person identification. A 3D wireframe model is fitted to face images using a robust objective function. Furthermore, we extract structural and textural information which is coupled with temoral information from the motion of local facial features. The extracted information is combined to form a feature vector descriptor for each image. This set of features has been tested on two databases for face recognition across facial expressions. We use Bayesian Network (BN) and Binary Decision Trees (BDT) as classifiers. The developed system is automatic, real-time capable and efficient.

Spatio-temporal multifeature for facial analysis

Human faces are 3D complex objects consisting of geometrical and appearance variations. They exhibit local and global variations when observed over time. In our daily life communication, human faces are seen in actions conveying a set of information during interaction. Cognitive science explains that human brains are capable of extracting this set of information very efficiently resulting in a better interaction with others. Our goal is to extract a single feature set which represents multiple facial characteristics. This problem is addressed by the analysis of different feature components on facial classifications using a 3D surface model. We propose a unified framework which is capable to extract multiple information from the human faces and at the same time robust against rigid and non-rigid facial deformations. A single feature vector corresponding to a given image is representative of persons identity, facial expressions, gender and age estimation. This feature set is called spatio-temporal multifeature (STMF) extracted from image sequences. An STMF is configured with three different feature components which is tested thoroughly to evidence its validity. The experimental results from four different databases show that this feature set provides high accuracy and at the same time exhibits robustness. The results have been discussed comparatively with different approaches.

Towards Unconstrained Face Recognition Using 3D Face Model

Over the last couple of decades, many commercial systems are available to identify human faces. However, face recognition is still an outstanding challenge against different kinds of real world variations especially facial poses, non-uniform lightings and facial expressions. Meanwhile the face recognition technology has extended its role from biometrics and security applications to human robot interaction (HRI). Person identity is one of the key tasks while interacting with intelligent machines/robots, exploiting the non intrusive system security and authentication of the human interacting with the system. This capability further helps machines to learn person dependent traits and interaction behavior to utilize this knowledge for tasks manipulation. In such scenarios acquired face images contain large variations which demands an unconstrained face recognition system.