Eigenface

About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.

Transform-Invariant PCA: A Unified Approach to Fully Automatic FaceAlignment, Representation, and Recognition

Abstract: We develop a transform-invariant PCA (TIPCA) technique which aims to accurately characterize the intrinsic structures of the human face that are invariant to the in-plane transformations of the training images. Specially, TIPCA alternately aligns the image ensemble and creates the optimal eigenspace, with the objective to minimize the mean square error between the aligned images and their reconstructions. The learning from the FERET facial image ensemble of 1,196 subjects validates the mutual promotion between image alignment and eigenspace representation, which eventually leads to the optimized coding and recognition performance that surpasses the handcrafted alignment based on facial landmarks. Experimental results also suggest that state-of-the-art invariant descriptors, such as local binary pattern (LBP), histogram of oriented gradient (HOG), and Gabor energy filter (GEF), and classification methods, such as sparse representation based classification (SRC) and support vector machine (SVM), can benefit from using the TIPCA-aligned faces, instead of the manually eye-aligned faces that are widely regarded as the ground-truth alignment. Favorable accuracies against the state-of-the-art results on face coding and face recognition are reported.

Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network

Abstract: Detection of immature peach fruits would help growers to create yield maps which are very useful tools for adjusting management practices during the fruit maturing stages. Machine vision algorithms were developed to detect and count immature peach fruit in natural canopies using colour images. This study was the first effort to detect immature peach fruit in natural environment to the authors’ knowledge. Captured images had various illumination conditions due to both direct sunlight and diffusive light conditions that make the fruit detection task more difficult. A training set and a validation set were used to develop and to test the algorithms. Different image scanning methods including finding potential fruit regions were developed and used to parse fruit objects in the natural canopy image. Circular Gabor texture analysis and ‘eigenfruit’ approach (inspired by the ‘eigenface’ face detection and recognition method) were used for feature extraction. Statistical classifiers, a neural network and a support vector machine classifier were built and used for detecting peach fruit. A blob analysis was performed to merge multiple detections for the same peach fruit. Performance of the classifiers and image scanning methods were introduced and evaluated. Using the proposed algorithms, 84.6, 77.9 and 71.2 % of the actual fruits were successfully detected using three different image scanning methods for the validation set.

A Local Region-based Approach to Gender Classi.cation From Face Images

Abstract: We present a novel appearance-based method for gender classification from face images. To circumvent the problem of local variations in appearance that may be caused by pose, expression, or illumination variability, we use local region analysis of the face to extract the gender classi?cation features. Given a new face image, a normalized feature vector is formed by matching N local regions of the face against some fixed set of M face images using the FaceIt algorithm, then applying the Karhunen-Loeve transform to reduce the dimensionality of this MN-dimensional vector. For the purpose of comparison, we have also implemented a holistic feature extraction method based on the well-known Eigenfaces. Gender classification is performed in a compact feature space via two standard binary classifiers; SVM and FLD. The classifier is tested via cross-validation on a database of approximately 13,000 frontal and nearly frontal face images, and the best performance of 94.2% is achieved with the local region-based feature extraction and SVM classification methods.

Face Recognition Using Principle Component Analysis, Eigenface and Neural Network

Abstract: Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is connection of two stages – Feature extraction using principle component analysis and recognition using the feed forward back propagation Neural Network. The algorithm has been tested on 400 images (40 classes). A recognition score for test lot is calculated by considering almost all the variants of feature extraction. The proposed methods were tested on Olivetti and Oracle Research Laboratory (ORL) face database. Test results gave a recognition rate of 97.018%