Partial & Holistic Face Recognition on FRGC-II data using Support Vector Machine Kernel Correlation Feature Analysis M. Savvides, R. Abiantun, J. Heo, S. Park, C.Xie and B.V.K. Vijayakumar, Electrical & Computer Eng Dept, Carnegie Mellon University, Pittsburgh PA 15213
[email protected], {raa, jheo, sungwonp, chunyanx}@andrew.cmu.edu,
[email protected]
Abstract In this paper we investigate how to perform face recognition on the hardest experiment (Exp4) in Face Recognition Grand Challenge(FRGC) phase-II data which deals with subjects captured under uncontrolled conditions such as harsh overhead illumination, some pose variations and facial expressions in both indoor and outdoor environments. Other variations include the presence and absence of eye-glasses. The database consists of a generic dataset of 12,776 images for training a generic face subspace, a target set of 16,028 images and a query set of 8,014 images are given for matching. We propose to use our novel face recognition algorithm using Kernel Correlation Feature Analysis for dimensionality reduction (222 features) coupled with Support Vector Machine discriminative training in the Target KCFA feature set for providing a similarity distance measure of the probe to each target subject. We show that this algorithm configuration yields the best verification rate at 0.1% FAR (87.5%) compared to PCA+SVM, GSLDA+SVM, SVM+SVM, KDA+SVM. Thus we explore with our proposed algorithm which facial regions provide the best discrimination ability, we analyze performing partial face recognition using the eye-region, nose region and mouth region. We empirically find that the eye-region is the most discriminative feature of the faces in FRGC data and yields a verification rate closest to the holistic face recognition of 83.5% @ 0.1% FAR compared to 87.5%. We use Support Vector Machines for fusing these two to boost the performance to
[email protected] % FAR on the first large-scale face database such as the FRGC dataset.
1. Introduction Machine recognition of human faces from still-images and video frames is an active research area due to the increasing demand for authentication in commercial and law enforcement applications. Despite the research advancement over the years, face recognition is still a highly challenging task in practice due to large nonlinear distortions caused by natural facial appearance distortions
such as expressions, pose and ambient illumination variations. Two well-known popular algorithms for face recognition are Eigenfaces [1] and Fisherfaces [2]. The Eigenfaces method generates features that capture the holistic nature of faces through Principal Component Analysis (PCA), which determines a lower-dimensional subspace that offers the minimum mean squared error approximation to the original high-dimensional face data. Instead of seeking a subspace that is efficient for representation, the Linear Discriminant Analysis (LDA) method seeks projection directions that are more optimal for discrimination. In practice, Fisherfaces first performs PCA to overcome the singularities in the within-class scatter matrix (Sw) by reducing the dimensionality of the data and then applies traditional LDA in this lowerdimensional subspace. Recently [3], it has been suggested that the null space of the Sw matrix is important for discrimination. The claim is that applying PCA in Fisherfaces may discard discriminative information since the null space of Sw contains the most discriminative information. Fueled by this insight, Direct LDA (DLDA) and Gram-Schmidt LDA (GSLDA) [4] methods have been proposed by utilizing part of the null-space of the Sw. However, these linear subspace methods[9] may not discriminate faces well due to large nonlinear distortions in the faces. In such cases, the proposed kernel correlation filter approach in this paper may be attractive because of its ability to tolerate some level of distortions [5][6]. One of the recent advances in advanced correlation filters is the class dependent feature analysis (CFA)[7] which proposes a novel feature extraction method using advanced correlation filter methods to provide an efficient compact feature set. We will show that with a proper choice of nonlinear kernel parameters with our proposed kernel correlation filter, the performance can be significantly improved over previous work. Our experimental evaluation includes results from CFA, GSLDA, Fisherfaces, Kernel LDA (KDA), Eigenfaces and Kernel PCA on a large scale database from the face recognition grand challenge (FRGC) dataset collected by the University of Notre Dame [8]. We examine our proposed methodology, the theory and derivation, how it performs
Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06) 0-7695-2646-2/06 $20.00 © 2006 IEEE
on holistic facial images and parts based recognition and fusion results.
this verification rate at a specified false acceptance rate of 0.1%, thus this paper will mostly focus on the verification rate at this operating point of the ROC curve.
1.2. Background
Figure 1. Example Query images from FRGC-Phase II dataset showing harsh uncontrolled illumination.
1.1. Face Recognition Grand Challenge (FRGC) The Face Recognition Grand Challenge(FRGC) is aimed to provide several face recognition experiment challenges to the face recognition community. In this paper we focus on the hardest 2D face recognition problem which is experiment 4 in the FRGC. This experiment test face recognition algorithms of subjects captured in uncontrolled indoor and outdoor conditions where there is considerable harsh lighting conditions for image capture as shown in Figure 1. The FRGC database consists of three dataset components: a “generic dataset” which is typically used to build a global face model (e.g. a global PCA subspace), a “target set” which consists of known people that we want to find and a “query” or “probe” set which are the unknown images captured that we need to identify against the gallery set. The generic training set consists of 222 people with up to a maximum of 64 images per person, yielding a total of 12,776 facial images for generic training. The gallery set consists of 466 people yielding a total of 16,028 target images, and the probe set also has 466 people with a total of 8,014 facial images. The subjects in the dataset were captured in many different sessions spanning a period of one year. Experiment 4 is difficult as the target images are controlled images with controlled lighting conditions whereas the probe images are captured in un-controlled indoor and outdoor setting posing a serious challenge. The NIST PCA baseline algorithm yields a verification rate of 12% at 0.1% false acceptance rate (FAR). It is the goal of the face recognition grand challenge(FRGC) to increase
One of the most common algorithms used is PCA or eigenfaces which is the reported baseline algorithm in FRGC. The PCA finds the minimum mean squared error linear transformation that maps from the original N dimensional data space into an M-dimensional feature subspace (where M
