2D and 3D face recognition: A survey

Presents results of the face verification contest that was organized in conjunction with International Conference on Pattern Recognition 2000. Participants had to use identical data sets from a large, publicly available multimodal database XM2VTSDB. Training and evaluation was carried out according to an a priori known protocol. Verification results of all tested algorithms have been collected and made public on the XM2VTSDB website, facilitating large scale experiments on classifier combination and fusion. Tested methods included, among others, representatives of the most common approaches to face verification -elastic graph matching, Fisher's linear discriminant and support vector machines.

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Comparison of face verification results on the XM2VTFS database

The use of hidden Markov models (HMM) for faces is motivated by their partial invariance to variations in scaling and by the structure of faces. The most significant facial features of a frontal face include the hair, forehead, eyes, nose and mouth. These features occur in a natural order, from top to bottom, even if the images undergo small rotations in the image plane, and/or rotations in the plane perpendicular to the image plane. Therefore, the image of a face may be modeled using a one-dimensional HMM by assigning each of these regions to a state. The observation vectors are obtained from the DCT or KLT coefficients. 
A one-dimensional HMM may be generalized, to give it the appearance of a two-dimensional structure, by allowing each state in a one-dimensional HMM to be a HMM. In this way, the HMM consists of a set of super states, along with a set of embedded states. Therefore, this is referred to as an embedded HMM. The super states may then be used to model two-dimensional data along one direction, with the embedded HMM modeling the data along the other direction. 
Both the standard HMM and the embedded HMM were tested for face recognition and detection. Compared to other methods, our proposed system offers a more flexible framework for face recognition and detection, and can be used more efficiently in scale invariant systems.

A hidden markov model-based approach for face detection and recognition

This paper introduces a novel method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in frontal view of facial images. Radial basis function (RBF) neural network with a hybrid learning algorithm (HLA) has been used as a classifier. The proposed feature extraction method includes human face localization derived from the shape information. An efficient distance measure as facial candidate threshold (FCT) is defined to distinguish between face and nonface images. Pseudo-Zernike moment invariant (PZMI) with an efficient method for selecting moment order has been used. A newly defined parameter named axis correction ratio (ACR) of images for disregarding irrelevant information of face images is introduced. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as RBF neural network learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL) indicate that the proposed method for human face recognition yielded a recognition rate of 99.3%.

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An efficient feature extraction method with pseudo-Zernike moment in RBF neural network-based human face recognition system

This paper introduces a novel method for the recognition of human faces in two-dimensional digital images using a new feature extraction method and Radial Basis Function (RBF) neural network with a Hybrid Learning Algorithm (HLA) as classifier. The proposed feature extraction method includes human face localization derived from the shape information using a proposed distance measure as Facial Candidate Threshold (FCT) as well as Pseudo Zernike Moment Invariant (PZMI) with a newly defined parameter named Correct Information Ratio (CIR) of images for disregarding irrelevant information of face images. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL) indicate that high order PZMI together with the derived face localization technique for extraction of feature data yielded a recognition rate of 99.3%.

An efficient human face recognition system using pseudo zernike moment invariant and radial basis function neural network

In this paper, we propose a new method for computerized human face recognition using fractal transformations. We show that by utilizing the intrinsic properties of block-wise self-similar transformations in fractal image coding we can use it to perform face recognition. The contractivity factor and the encoding scheme of the fractal encoder are shown to affect recognition rates. Using this method, an average error rate of 1.75% was obtained on the ORL face database.

T. Tan

Papers

Face recognition by fractal transformations