Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

BDPCA plus LDA : a novel fast feature extraction technique for face recognition

2005-2006 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Face Recognition Methodologies Using Component Analysis: The Contemporary Affirmation of The Recent Literature

This paper explored the contemporary affirmation of the recent literature in the context of face recognition systems, a review motivated by contradictory claims in the literature. This paper shows how the relative performance of recent claims based on methodologies such as PCA and ICA, which are depend on the task statement. It then explores the space of each model acclaimed in recent literature. In the process, this paper verifies the results of many of the face recognition models in the literature, and relates them to each other and to this work

Subspace Methods for Face Recognition: Singularity, Regularization, and Robustness

Face recognition has been an important issue in computer vision and pattern recognition over the last several decades (Zhao et al., 2003). While human can recognize faces easily, automated face recognition remains a great challenge in computer-based automated recognition research. One difficulty in face recognition is how to handle the variations in expression, pose an

Evaluation of face recognition algorithms under noise

One of the major applications of computer vision and image processing is face recognition, where a computerized algorithm automatically identifies a person’s face from a large image dataset or even from a live video. This thesis addresses facial recognition, a topic that has been widely studied due to its importance in many applications in both civilian and military domains. The application of face recognition systems has expanded from security purposes to social networking sites, managing fraud, and improving user experience. Numerous algorithms have been designed to perform face recognition with good accuracy. This problem is challenging due to the dynamic nature of the human face and the different poses that it can take. Regardless of the algorithm, facial recognition accuracy can be heavily affected by the presence of noise. This thesis presents a comparison of traditional and deep learning face recognition algorithms under the presence of noise. For this purpose, Gaussian and salt-andpepper noises are applied to the face images drawn from the ORL Dataset. The image recognition is performed using each of the following eight algorithms: principal component analysis (PCA), two-dimensional PCA (2D-PCA), linear discriminant analysis (LDA), independent component analysis (ICA), discrete cosine transform (DCT), support vector machine (SVM), convolution neural network (CNN) and Alex Net. The ORL dataset was used in the experiments to calculate the evaluation accuracy for each of the investigated algorithms. Each algorithm is evaluated with two experiments; in the first experiment only one image per person is used for training, whereas in the second experiment, five images per person are used for training. The investigated traditional algorithms are implemented with MATLAB and the deep learning algorithms approaches are implemented with Python. The results show that the best performance was obtained using the DCT algorithm with 92% dominant eigenvalues and 95.25 % accuracy, whereas for deep learning, the best performance was using a CNN with accuracy of 97.95%, which makes it the best choice under noisy conditions

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