91,689 research outputs found
Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition
This paper presents a novel quadratic projection based feature extraction
framework, where a set of quadratic matrices is learned to distinguish each
class from all other classes. We formulate quadratic matrix learning (QML) as a
standard semidefinite programming (SDP) problem. However, the con- ventional
interior-point SDP solvers do not scale well to the problem of QML for
high-dimensional data. To solve the scalability of QML, we develop an efficient
algorithm, termed DualQML, based on the Lagrange duality theory, to extract
nonlinear features. To evaluate the feasibility and effectiveness of the
proposed framework, we conduct extensive experiments on biometric recognition.
Experimental results on three representative biometric recogni- tion tasks,
including face, palmprint, and ear recognition, demonstrate the superiority of
the DualQML-based feature extraction algorithm compared to the current
state-of-the-art algorithm
Optimizing Face Recognition Using PCA
Principle Component Analysis PCA is a classical feature extraction and data
representation technique widely used in pattern recognition. It is one of the
most successful techniques in face recognition. But it has drawback of high
computational especially for big size database. This paper conducts a study to
optimize the time complexity of PCA (eigenfaces) that does not affects the
recognition performance. The authors minimize the participated eigenvectors
which consequently decreases the computational time. A comparison is done to
compare the differences between the recognition time in the original algorithm
and in the enhanced algorithm. The performance of the original and the enhanced
proposed algorithm is tested on face94 face database. Experimental results show
that the recognition time is reduced by 35% by applying our proposed enhanced
algorithm. DET Curves are used to illustrate the experimental results.Comment: 9 page
FACE RECOGNITION IN EIGEN DOMAIN WITH NEURO-FUZZY CLASSIFIER AND EVOLUTIONARY OPTIMIZATION
Face Recognition is a nascent field of research with many challenges. The proposed system focuses on recognizing faces in a faster and more accurate way using eigenface approach and genetic algorithm by considering the entire problem as an optimization problem. It consists of two stages: Eigenface approach is used for feature extraction and genetic algorithm based feed forward Neuro-Fuzzy System is used for face recognition. Classification of face images to a particular class is done using an artificial neural network. The training of neural network is done using genetic algorithm, a machine learning approach which optimizes the weights used in the neural network. This is an efficient optimization technique and an evolutionary classification method. The algorithm has been tested on 200 images (20 classes). A recognition score for test lot is calculated by considering almost all the variants of feature extraction. Test results gave a recognition rate of 97.01%
Effective Face Feature For Human Identification
Face image is one of the most important parts of human body. It is easily use for identification process. People naturally identify one another through face images. Due to increase rate of insecurity in our society, accurate machine based face recognition systems are needed to detect impersonators. Face recognition systems comprise of face detector module, preprocessing unit, feature extraction subsystem and classification stage. Robust feature extraction algorithm plays major role in determining the accuracy of intelligent systems that involves image processing analysis. In this paper, pose invariant feature is extracted from human faces. The proposed feature extraction method involves decomposition of captured face image into four sub-bands using Haar wavelet transform thereafter shape and texture features are extracted from approximation and detailed bands respectively. The pose invariant feature vector is computed by fusing the extracted features. Effectiveness of the feature vector in terms of intra-person variation and inter-persons variation was obtained from feature plot
Structural Geodesic-Tchebychev Transform: An image similarity measure for face recognition
This work presents a new holistic measure for face recognition. Face recognition involves three steps: Face Detection, Feature Extraction and Matching. In the face detection process to identify the face area in face images, Viola-Jones algorithm has been used. Feature extraction is based on performing double-transformation, where discrete Tchebychev transform is performed on the geodesic distance transform of the grayscale image. Structural Similarity (SSIM) is applied to the resulting image double-transform to find matching factor with other image faces in the FEI (Brazilian) database. Performance is measured using a confidence criterion based on the similarity distance between the recognized person (best match) and the next possible ambiguity (second-best match). Simulation results showed that the proposed approach handles the face recognition efficiently as compared with SSIM
- …