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Electrocardiogram (ECG) signals have distinct features of the electrical activity of the heart which are unique among individuals and have recently emerged as a potential biometric tool for human identification. The paper attempts to address the problem of ECG identification based on non-fiducial approach using unsupervised classifier and a Deep Learning approaches. This work investigates the ability of local binary pattern to extract the significant pattern/feature that describes the heartbeat activity for each person’s ECG and the use of staked autoencoders and deep belief network to further enhance the extracted features and classify them based on their heartbeat activity. The proposed approach is validated using experimental datasets from two publicly available databases MIT-BIH Normal Sinus Rhythm and ECG-ID and the results demonstrate the effectiveness of this approach for ECG-based human authentication.

paper, we present an new approach to face recognition based on the combination of feature extraction methods, such as two-dimensional DWT-2DPCA and DWT-2DLDA, with a probabilistic neural networks. This later is used to classify the features matrix extracts for space data created by Two- dimensional Subspace Analysis .The technique 2D-DWT is used to eliminate the illumination ,noise and redundancy of face in order to reduce calculations of the probabilistic neural network operations ,and improve a face recognition system in accuracy and computation time. The proposed approach is tested on ORL and FEI face databases. Experimental results on this databases demonstrated the effectiveness of the proposed approach for face recognition with high accuracy compared with previous methods..

Part 2: Computational Intelligence: Object Recognition and Authentification; International audience; Face recognition is considered as one of the best biometric methods used for human identification and verification; this is because of its unique features that differ from one person to another, and its importance in the security field. This paper proposes an algorithm for face recognition and classification using a system based on WPD, fractal codes and two-dimensional subspace for feature extraction, and Combined Learning Vector Quantization and PNN Classifier as Neural Network approach for classification. This paper presents a new approach for extracted features and face recognition .Fractal codes which are determined by a fractal encoding method are used as feature in this system. Fractal image compression is a relatively recent technique based on the representation of an image by a contractive transform for which the fixed point is close to the original image. Each fractal code consists of five parameters such as corresponding domain coordinates for each range block. Brightness offset and an affine transformation. The proposed approach is tested on ORL and FEI face databases. Experimental results on this database demonstrated the effectiveness of the proposed approach for face recognition with high accuracy compared with previous methods.

Face recognition is considered as one of the best biometric methods used for human identification and verification; this is because of its unique features that differ from one person to another, and its importance in the security field. This paper proposes an algorithm for face recognition and classification using a system based on WPD, fractal codes and two-dimensional subspace for feature extraction, and Combined Learning Vector Quantization and PNN Classifier as Neural Network approach for classification. This paper presents a new approach for extracted features and face recognition. Fractal codes which are determined by a fractal encoding method are used as feature in this system. Fractal image compression is a relatively recent technique based on the representation of an image by a contractive transform for which the fixed point is close to the original image. Each fractal code consists of five parameters such as corresponding domain coordinates for each range block. Brightness offset and an affine transformation. The proposed approach is tested on ORL and FEI face databases. Experimental results on this database demonstrated the effectiveness of the proposed approach for face recognition with high accuracy compared with previous methods.

In this paper, we present an new approach to face recognition based on the combination of feature extraction methods, such as two-dimensional DWT-2DPCA and DWT-2DLDA, with a probabilistic neural networks. The technique 2D-DWT is used to eliminate the illumination, noise and redundancy of a face in order to reduce calculations of the probabilistic neural network operations.