Conference Paper

Face Recognition Based on Discriminant Evaluation in the Whole Space

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ.
DOI: 10.1109/ICASSP.2007.366218 Conference: Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on, Volume: 2
Source: IEEE Xplore


This paper proposes a face recognition approach that performs linear discriminant analysis in the whole eigenspace. It decomposes the eigenspace into two subspaces: a reliable subspace spanned mainly by the facial variation and an unstable subspace due to finite number of training samples. Eigenvalues in the unstable subspace are replaced by a constant. This alleviates the over-fitting problem and enables the discriminant evaluation in the whole space. Feature extraction or dimensionality reduction occurs only at the final stage after the discriminant assessment. These efforts facilitate a discriminative and stable low-dimensional feature representation of the face image. Experimental results comparing some popular subspace methods on FERET and ORL databases show that our approach consistently outperforms others.

Download full-text


Available from: Xudong Jiang
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Numerous face recognition algorithms use principal component analysis (PCA) as the first step for dimensionality reduction (DR) followed by linear discriminant analysis (LDA). PCA is applied in the beginning because it performs the DR in the minimum square error sense and achieves the most compact representation of data. However, they lack discrimination ability. To optimize classification, LDA and its variants are applied to the PCA reduced subspace so that the transformed data achieves minimum within-class variation and maximum between-class variations. In this paper, we study total, within-class and between-class scatter matrices and their roles in DR or feature extraction with good discrimination ability. The number of dimensions retained in DR plays a very crucial role for subsequent discriminant analysis. We reveal some important aspect of how recognition rate varies using different scatter matrices and their stepwise DR. Experimental results on popular face databases are provided to support our findings.
    Full-text · Conference Paper · Jan 2008
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Face verification is different from face identification task. Some traditional subspace methods that work well in face identification may suffer from severe over-fitting problem when applied for the verification task. Conventional dis-criminative methods such as linear discriminant analysis (LDA) and its variants are highly sensitive to the training data, which hinders them from achieving high verification accuracy. This work proposes an eigenspectrum model that allevi-ates the over-fitting problems by replacing the unreliable small and zero eigenvalues with the model values. It also enables the discriminant evaluation in the whole space to extract the low dimensional features effectively. The proposed approach is evaluated and compared with 8 popular subspace based methods for a face verification task. Experimental results on three face databases show that the proposed method consistently outperforms others.
    Full-text · Article · Jun 2008 · The Open Artificial Intelligence Journal
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Feature extraction and classifier design are two main processing blocks in all pattern recognition and computer vision systems. For visual patterns, extracting robust and discriminative features from image is the most difficult yet the most critical step. Several typical and advanced approaches of feature extraction from image are explored, some of which are analyzed in depth. Various techniques of feature extraction from image are organized in four categories: human expert knowledge based methods, image local structure based approaches, image global structure based techniques and machine learning based statistical approaches. We will show examples of applying these feature extraction approaches to solve problems of the image based biometrics, including fingerprint verification/identification and face detection/recognition. These illustrative application examples unveil the ideas, principles and advancements of feature extraction techniques and demonstrate their effectiveness and limitations in solving real-world problems.
    Full-text · Article · Jan 2009
Show more