152 research outputs found

    Mixture of Bilateral-Projection Two-dimensional Probabilistic Principal Component Analysis

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    The probabilistic principal component analysis (PPCA) is built upon a global linear mapping, with which it is insufficient to model complex data variation. This paper proposes a mixture of bilateral-projection probabilistic principal component analysis model (mixB2DPPCA) on 2D data. With multi-components in the mixture, this model can be seen as a soft cluster algorithm and has capability of modeling data with complex structures. A Bayesian inference scheme has been proposed based on the variational EM (Expectation-Maximization) approach for learning model parameters. Experiments on some publicly available databases show that the performance of mixB2DPPCA has been largely improved, resulting in more accurate reconstruction errors and recognition rates than the existing PCA-based algorithms

    Towards Practical Face Recognition System Employing Row-Based Distance Method In 2dpca Based Algorithms

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    Automatic face recognition has been a focus research topic in past few decades. This is due to the advantages of face recognition and the potential need for high security in commercial and law enforcement applications. However, due to nature of the face, it is subjected to several variations. Thus, finding a good face recognition system is still an active research field till today. Many approaches have been proposed to overcome the face variations. In the midst of these techniques, subspace methods are considered the most popular and powerful techniques. Among them, eigenface or Principal Component Analysis (PCA) method is considered as one of the most successful techniques in subspace methods. One of the most important extensions of PCA is Two-dimensional PCA (2DPCA). However, 2DPCA-based features are matrices rather than vectors as in PCA. Hence, different distance computation methods have been proposed to calculate the distance between the test feature matrix and the training feature matrices. All previous methods deal with the classification problem mathematically without any consideration between feature matrices and the face images. Besides, the system performance in practical applications relies on the number of eigenvectors chosen. As a solution to the above mentioned issues, four new distance methods have been proposed in this thesis, which are based on the rows of a feature matrix of 2DPCA-based algorithms. Through experiments, using eight face databases, their improvements compared to the previous distance methods are demonstrated

    Evaluation of face recognition algorithms under noise

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    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

    画像情報を利用した複数識別統合による性別と年齢層の識別

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    制度:新 ; 文部省報告番号:甲2483号 ; 学位の種類:博士(工学) ; 授与年月日:2007/7/26 ; 早大学位記番号:新459

    Novel Facial Image Recognition Techniques Employing Principal Component Analysis

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    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

    Heterogeneous Techniques used in Face Recognition: A Survey

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    Face Recognition has become one of the important areas of research in computer vision. Human Communication is a combination of both verbal and non-verbal. For interaction in the society, face serve as the primary canvas used to express distinct emotions non-verbally. The face of one person provides the most important natural means of communication. In this paper, we will discuss the various works done in the area of face recognition where focus is on intelligent approaches like PCA, LDA, DFLD, SVD, GA etc. In the current trend, combination of these existing techniques are being taken into consideration and are discussed in this paper.Keywords: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Genetic Algorithm (GA), Direct Fractional LDA (DFLD
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