Two-dimensional PCA : a new approach to appearance-based face representation and recognition

2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Assessing a Template Matching Approach for Tree Height and Position Extraction from Lidar-Derived Canopy Height Models of Pinus Pinaster Stands

In this paper, an assessment of a method using a correlation filter over a lidar-derived digital canopy height model (CHM) is presented. The objective of the procedure is to obtain stem density, position, and height values, on a stand with the following characteristics: ellipsoidal canopy shape (Pinus pinaster), even-aged and single-layer structure. The process consists of three steps: extracting a correlation map from CHM by applying a template whose size and shape resembles the canopy to be detected, applying a threshold mask to the correlation map to keep a subset of candidate-pixels, and then applying a local maximum filter to the remaining pixel groups. The method performs satisfactorily considering the experimental conditions. The mean tree extraction percentage is 65% with a coefficient of agreement of 0.4. The mean absolute error of height is ~0.5 m for all plots except one. It can be considered a valid approach for extracting tree density and height in regularly spaced stands (i.e., poplar plantations) which are fundamental for extracting related forest parameters such as volume and biomass

Recognition of unfamiliar faces

People are excellent at identifying faces familiar to them, even from very low quality images, but are bad at recognising, or even matching, faces that are unfamiliar. In this review we shall consider some of the factors which affect our abilities to match unfamiliar faces. Major differences in orientation (e.g. inversion) or greyscale information (e.g. negation) affect face processing dramatically, and such effects are informative about the nature of the representations derived from unfamiliar faces, suggesting that these are based on relatively low-level image descriptions. Consistent with this, even relatively minor differences in lighting and viewpoint create problems for human face matching, leading to potentially important problems over the use of images from security video images. The relationships between different parts of the face (its "configuration") are as important to the impression created of an upright face as local features themselves, suggesting further constraints on the representations derived from faces. The review then turns to consider what computer face recognition systems may contribute to understanding both the theory and the practical problems of face identification. Computer systems can be used as an aid to person identification, but also in an attempt to model human perceptual processes. There are many approaches to computer recognition of faces, including ones based on low-level image analysis of whole face images, which have potential as models of human performance. Some systems show significant correlations with human perceptions of the same faces, for example recognising distinctive faces more easily. In some circumstances, some systems may exceed human abilities on unfamiliar faces. Finally, we look to the future of work in this area, that will incorporate motion and three-dimensional shape information

3D Model Based Pose Invariant Face Recognition from a Single Frontal View

This paper proposes a 3D model based pose invariant face recognition method that can recognize a face of a large rotation angle from its single nearly frontal view. The proposed method achieves the goal by using an analytic-to-holistic approach and a novel algorithm for estimation of ear points. Firstly, the proposed method achieves facial feature detection, in which an edge map based algorithm is developed to detect the ear points. Based on the detected facial feature points 3D face models are computed and used to achieve pose estimation. Then we reconstruct the facial feature points' locations and synthesize facial feature templates in frontal view using computed face models and estimated poses. Finally, the proposed method achieves face recognition by corresponding template matching and corresponding geometric feature matching. Experimental results show that the proposed face recognition method is robust for pose variations including both seesaw rotations and sidespin rotations

PCA-based finger movement and grasping classification using data glove “Glove MAP”

nowadays, fingers movement and hand gestures can be used as main activities in translating by naturally and convenient way to the human computer interaction.The purpose of this paper is to analyze in depth the thumb, index and middle fingers on the hand grasping movement against an object.The classification of the fingers activities is analyzed using the statistical analysis method. Principal Component Analysis (PCA) is one of the methods that able to reduce the dimensional dataset of hand motion as well as measure the capacity of the fingers movement.The fingers movement is estimated from the bending representative of proximal and intermediate phalanges of thumb, index and middle fingers. The effectiveness of the propose assessment analysis were shown through the experiments of three fingers motions.Preliminary results of this experiment showed that the use of the first and second principal components can allow distinguishing between three fingers grasping movements

Novel Facial Image Recognition Techniques Employing Principal Component Analysis

Recently, pattern recognition/classification has received considerable attention in diverse engineering fields such as biomedical imaging, speaker identification, fingerprint recognition, and face recognition, etc. This study contributes novel techniques for facial image recognition based on the Two dimensional principal component analysis in the transform domain. These algorithms reduce the storage requirements by an order of magnitude and the computational complexity by a factor of 2 while maintaining the excellent recognition accuracy of the recently reported methods. The proposed recognition systems employ different structures, multicriteria and multitransform. In addition, principal component analysis in the transform domain in conjunction with vector quantization is developed which result in further improvement in the recognition accuracy and dimensionality reduction. Experimental results confirm the excellent properties of the proposed algorithms

Face recognition under partial occlusion and small dense noise

Problem of automatic recognition of human faces from front views with varying expression, illumination, occlusion as well as disguise is considered. Here the problem of recognition is cast as one of the several classifying linear regression models and argued that in handling such problems a new theory using sparse representation of signals is the key. A face recognition algorithm is also introduced which uses ‘L1-minimization’ theory of optimization. This proposed concept handles two crucial problems of face recognition, which are, feature extraction and robust occlusion handling. For extraction of features, PCA is used, but later in the thesis it is shown that if sparsity is properly calculated in the face representation, selection of features doesn’t remain crucial. However, the number of extracting features is crucial here. Another crucial factor is the authenticity of calculating sparse coefficients. Unconventional feature extraction techniques such as down-sampled images and random projections give results comparable to common features like Eigenfaces, as long as the dimension of the feature space exceeds a particular threshold, predicted by the sparse representation theory. This can handle errors because of occlusion and consistently by using the fact that these errors are frequently sparse with respect to the standard basis. The sparse representation theory helps in predicting that how much of occlusion can be handled using this recognition algorithm and how can the training images be selected so that robustness to occlusion can be maximized. A Number of experiments on freely accessible facial databases are performed to justify the efficiency of the proposed algorithm and the above claims