Pang Ying Han

Random Projection with Robust Linear Discriminant Analysis Model in Face Recognition

This paper presents a face recognition technique with two techniques: random projection (RP) and robust linear discriminant analysis model (RDM). RDM is an enhanced version of fishers linear discriminant with energy-adaptive regularization criteria. It is able to yield better discrimination performance. Same as Fishers Linear Discriminant, it also faces the singularity problem of within-class scatter. Thus, a dimensionality reduction technique, such as principal component analysis (PCA), is needed to deal with this problem. In this paper, RP is used as an alternative to PCA in RDM in the application of face recognition. Unlike PCA, RP is training data independent and the random subspace computation is relatively simple. The experimental results illustrate that the proposed algorithm is able to attain better recognition performance (error rate is approximately 5% lower) compared to Fisherfaces.

Neighbourhood Discriminant Locally Linear Embedding in Face Recognition

Face images are often very high-dimensional and complex. However, the actual underlying structure can be characterized by a small number of features. Hence, locally linear embedding (LLE) is proposed as a nonlinear dimension reduction technique to deal this problem. LLE learns the intrinsic manifold embedded in the high dimensional ambient space by minimizing the global reconstruction error of the neighbourhood in the data set. LLE is popular in analyzing face images with different poses, illuminations or facial expressions for one subject class. It is developed based on the assumption that data that is distributed on a single manifold is having the same class label; hence the process of neighborhood selection is non class-specific. However, this is inappropriate to face recognition as face recognition learns in multiple manifolds where each representing data on one specific class. Here, we modify the original LLE by embedding prior class information in the process of neighborhood selection. Experimental results demonstrate that our technique consistently outperforms the original LLE in ORL, PIE and FRGC databases.

Eigenvector Weighting Function in Face Recognition

Graph-based subspace learning is a class of dimensionality reduction technique in face recognition. The technique reveals the local manifold structure of face data that hidden in the image space via a linear projection. However, the real world face data may be too complex to measure due to both external imaging noises and the intra-class variations of the face images. Hence, features which are extracted by the graph-based technique could be noisy. An appropriate weight should be imposed to the data features for better data discrimination. In this paper, a piecewise weighting function, known as Eigenvector Weighting Function (EWF), is proposed and implemented in two graph based subspace learning techniques, namely Locality Preserving Projection and Neighbourhood Preserving Embedding. Specifically, the computed projection subspace of the learning approach is decomposed into three partitions: a subspace due to intra-class variations, an intrinsic face subspace, and a subspace which is attributed to imaging noises. Projected data features are weighted differently in these subspaces to emphasize the intrinsic face subspace while penalizing the other two subspaces. Experiments on FERET and FRGC databases are conducted to show the promising performance of the proposed technique.

Face recognition via semi-supervised discriminant local analysis

Semi-supervised learning approach is a fusion approach of supervised and unsupervised learning. Semi-supervised approach performs data learning from a limited number of available labelled training images along with a large pool of unlabelled data. Semi-supervised discriminant analysis (SDA) is one of the popular semi-supervised techniques. However, there is room for improvement. SDA resides in the illumination and local change of the face features. Hence, it is hardly to guarantee its performance if there are illumination and local changes on the images. This paper presents an improved version of SDA, termed as Semi-Supervised Discriminant Local Analysis (SDLA). In this proposed technique, a local descriptor is amalgamated with SDA. Hence, SDLA could possess the capabilities of both the local descriptor and SDA, in such a way that SDLA utilizes limited number of labelled training data and huge pool of unlabelled data to optimally capture local discriminant features of face data. The empirical results demonstrate that SDLA shows promising performance in both normal and makeup face authentication.

Semi-supervised generic descriptor in face recognition

Supervised learning techniques are preferable in face recognition for their pleasant data discriminating capability. However, their performance just can be assured if and only if there are sufficient labelled training images available. Practically, it always happens that only a small number of labelled training images available due to costly and time consuming labelling process. On the other hand, a large pool of unlabeled data could be easily obtained through public databases like Google or Flickr. Hence, semi-supervised learning is an alternative direction in face recognition. Semi-supervised techniques utilize limited labelled training images and huge amount of unlabeled training data for data learning. This paper presents a new semi-supervised technique, namely Semi-supervised Generic Descriptor (SSGD). SSGD uses labelled training images to compute the null space of class scatter vector and generate class generic descriptors to represent each class. Besides that, unlabelled training images are exploited to obtain more information about face data structure. The empirical results demonstrate that SSGD shows relatively promising performance in face verification.

Independent Statistical Descriptor in Face Recognition

This paper devises a filter bank approach to extract the local structure knowledge of a face image by computing its probability distribution function from the filter responses. Independent Component Analysis (ICA) filters are embraced in this work. Considering the limitation of ICA filter learning in handling the image conditions with uncontrolled facial expressions, illuminations, aging, etc., we proposed an independent statistical descriptor, coined ISD, in this paper with the aim to handle the rampant images. ISD intensifies ICA response invariance through hashing the filter responses by encoding the relation between each response element and its neighbour and then block-wise histogramming the output. In addition, an overlapping average pooling is executed to regulate the histogram features, prior to whitening PCA compression. The good performance of ISD descriptors has been extensively corroborated in the empirical results on face recognition.

Intelligent web crawler for file safety inspection

The Internet has always been growing with all the contents and information added by different types of users. Without proper storage and indexing, these contents can easily be lost in the sea of information housed by the Internet. Hence, an automated program, known as the web crawler is used to index all the contents added to the Internet. With proper configurations and settings, a web crawler can be used for other purposes besides web indexing, which include downloading files from the web. Millions or billions of files are uploaded on the Internet and for most of the sites which host these files, there are no direct indication of whether the file is safe and free of malicious codes. Therefore, this paper aims to provide a construction of a web crawler which crawls all the pages in a given website domain, and download all the possible downloadable files linked to those pages, for the purpose of file safety inspection.

Can subspace based learning approach perform on makeup face recognition

The impacts of facial makeup on automated face recognition system have received attention recently and studies have shown that facial cosmetics can compromise the accuracy of current face recognition techniques. Hence, there are groups of researchers endeavoring to develop the face recognition systems that are robust to facial makeup. In this work, the literatures on various techniques proposed to deal with facial makeup are reviewed. At the same time, we present the findings of subspace based learning approach in makeup face recognition the performance comparison of local descriptors and subspace learning approaches.

Wavelet Based SDA for Face Recognition

Semi-supervised discriminant analysis (SDA) is a popular semi-supervise learning technique for limited labelled training sample problem in face recognition. However, SDA resides in the illumination variations and noise of the face features. Hence, SDA exposes the illumination variations and noise when constructing the optimal projection. It could affect the projection, leading to poor performance. In this paper, an enhanced SDA, namely Wavelet SDA, is proposed. This proposed technique is to resolve the problem of intra-class variations due to illumination variations and noise on image data. The robustness of the proposed technique is evaluated using three well-known face databases, i.e. ORL, FERET and FRGC. Empirical results validated the good effects of wavelet transform on SDA, leading to better recognition performance.

Discriminative Discriminant Common Vector in face verification

Discriminant Common Vectors (DCV) is proposed to solve small sample size problem. Face recognition encounters this dilemma where number of training samples is always smaller than the data dimension. In literature, it is shown that DCV is efficient in face recognition. In this paper, DCV is enhanced for further boosting its discriminating power. This modified version is namely Discriminative Discriminant Common Vectors (DDCV). In this technique, a local Laplacian matrix of face data is computed. This matrix is used to derive a regularization model for computing discriminative class common vectors. Experimental results demonstrate that DDCV illustrates its effectiveness on face verification, especially on facial images with significant intra class variations.