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Pose Invariant Face Recognition Using Pca

2015-10-20
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Release : 2015-10-20
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Book Rating : 949/5 ( reviews)

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Book Synopsis Pose Invariant Face Recognition Using Pca by : Patel Nehal

Download or read book Pose Invariant Face Recognition Using Pca written by Patel Nehal. This book was released on 2015-10-20. Available in PDF, EPUB and Kindle. Book excerpt: Both face detection and recognition are very curious areas in the field of image analysis, computer vision and pattern recognition that has received a big deal of attention over the last few years. It has been widely used for the purpose of security and forensic science for identify of an individual e.g. at the place of video surveillance, airports, traffic, terrorist attacks.To analyze the information of face images: faster, robust and efficient face detection and recognition algorithms are required. This system has been facing problems in recognizing subjects of varying poses, illumination conditions, facial expressions, and face occlusions. Due to variation in pose relative to camera certain features like smile, open eyes or mouth, left side or right side of mouth or eyes, occluded mouth or eyes can't be detected and extracted properly. It will be a critical task to detect a person with varying poses in vertical direction. In this work we present, face detection is performed by skin tone. Through PCA extract features and system is getting trained and tested. For face recognition process, Euclidean distance is measured and based on that minimum distance face is recognized

Improvement of Face Recognition Using Principal Component Analysis and Moment Invariant

2007
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Release : 2007
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Book Synopsis Improvement of Face Recognition Using Principal Component Analysis and Moment Invariant by : Annie Thomas

Download or read book Improvement of Face Recognition Using Principal Component Analysis and Moment Invariant written by Annie Thomas. This book was released on 2007. Available in PDF, EPUB and Kindle. Book excerpt: Face recognition attracts many researchers and has made significant progress in recent years. Face recognition is a type of biometric just like fingerprint and iris scans. This technology plays an important role in real-world applications, such as commercial and law enforcement applications, from here comes the importance of tackling this kind of research. In this research, we have proposed a method that integrates Principal Component Analysis (PCA) and Moment Invariant with face colour in gray scale to recognize face images of various pose. The PCA method is used to analyze the face image because it is optimal with any similar face image analysis and it has been employed to extract the global information. The vectors of a face in the database that are matched with the one of face image will be recognized the owner. If the vector is not matched, the original face image will be reconsidered with moment invariant and face colour in gray scale extraction. Then, the face will be rematched. In this way, the unrecognized faces will be reconsidered again and some will be recognized accurately to increase the number of recognized faces and improve the recognition accuracy as well. We have applied our method on Olivetti Research Laboratory (ORL) database which is issued by AT&T. The database contains 40 different faces images with 10 each face. Our experiment is done by using the holdout to measure the recognition accuracy, as we divided about 2/3 of the data 280 faces for training, and about 1/3 which is 120 faces for testing. The results showed a recognition accuracy of 94% for applying PCA, and 96% after reconsidering the unrecognized patterns by dealing with pose-varied faces and face colour extraction. Our proposed method has improved the recognition accuracy with the additional features extracted (PCA + face colour in gray scale) with the consideration of the total time process.

Soft Computing: Theories and Applications

2020-08-14 Technology & Engineering
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Release : 2020-08-14
Genre : Technology & Engineering
Kind : eBook
Book Rating : 318/5 ( reviews)

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Book Synopsis Soft Computing: Theories and Applications by : Millie Pant

Download or read book Soft Computing: Theories and Applications written by Millie Pant. This book was released on 2020-08-14. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on soft computing and how it can be applied to solve real-world problems arising in various domains, ranging from medicine and healthcare, to supply chain management, image processing and cryptanalysis. It gathers high-quality papers presented at the International Conference on Soft Computing: Theories and Applications (SoCTA 2019), organized by the National Institute of Technology Patna, India. Offering valuable insights into soft computing for teachers and researchers alike, the book will inspire further research in this dynamic field.

A Dynamic Approach to Pose Invariant Face Identification Using Cellular Simultaneous Recurrent Networks

2011
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Release : 2011
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Book Synopsis A Dynamic Approach to Pose Invariant Face Identification Using Cellular Simultaneous Recurrent Networks by : Teddy Salan

Download or read book A Dynamic Approach to Pose Invariant Face Identification Using Cellular Simultaneous Recurrent Networks written by Teddy Salan. This book was released on 2011. Available in PDF, EPUB and Kindle. Book excerpt: Face recognition is a widely covered and desirable research field that produced multiple techniques and different approaches. Most of them have severe limitations with pose variations or face rotation. The immediate goal of this thesis is to deal with pose variations by implementing a face recognition system using a Cellular Simultaneous Recurrent Network (CSRN). The CSRN is a novel bio-inspired recurrent neural network that mimics reinforcement learning in the brain. The recognition task is defined as an identification problem on image sequences. The goal is to correctly match a set of unknown pose distorted probe face sequences with a set of known gallery sequences. This system comprises of a pre-processing stage for face and feature extraction and a recognition stage to perform the identification. The face detection algorithm is based on the scale-space method combined with facial structural knowledge. These steps include extraction of key landmark points and motion unit vectors that describe movement of face sequqnces. The identification process applies Eigenface and PCA and reduces each image to a pattern vector used as input for the CSRN. In the training phase the CSRN learns the temporal information contained in image sequences. In the testing phase the network predicts the output pattern and finds similarity with a test input pattern indicating a match or mismatch.Previous applications of a CSRN system in face recognition have shown promise. The first objective of this research is to evaluate those prior implementations of CSRN-based pose invariant face recognition in video images with large scale databases. The publicly available VidTIMIT Audio-Video face dataset provides all the sequences needed for this study. The second objective is to modify a few well know standard face recognition algorithms to handle pose invariant face recognition for appropriate benchmarking with the CSRN. The final objective is to further improve CSRN face recognition by introducing motion units which can be used to capture the direction and intensity of movement of feature points in a rotating face.

Invariant Face Recognition in Hyperspectral Images

2014
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Release : 2014
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Book Rating : 258/5 ( reviews)

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Book Synopsis Invariant Face Recognition in Hyperspectral Images by : Han Wang

Download or read book Invariant Face Recognition in Hyperspectral Images written by Han Wang. This book was released on 2014. Available in PDF, EPUB and Kindle. Book excerpt: The performance of current face recognition systems has reached a satisfactory level under controlled conditions. However, when conditions are not controlled, the performance degrades dramatically. This study considers the challenges introduced by variations in expression, pose, and illumination. Existing methods use either spatial or spectral information. In this study, we propose algorithms that make use of spatial and spectral information simultaneously. Spectral features are extracted from hyperspectral images to represent subjects' spectral characteristics. Spatial features are extracted from one or more bands of a hyperspectral image. For expression-invariant recognition, we extract spectral features from three tissue types. We also design a set of 3D Gabor filters to represent spatial and spectral correlations for use as features. We then apply principal component analysis (PCA) to these features to model expression variation. For pose-invariant recognition, we also extract spectral features from three tissue types. 3D face models are learned using correspondences between a generic 3D model and 2D images. We then use the 3D models to synthesize images under novel poses. Next, we design a set of 2D Gabor filters to extract spatial features. We also apply PCA to correspondences to extract features. For illumination-invariant recognition, a basis is learned that is able to represent a variety of illumination conditions. The images are filtered to alleviate shadow effects and a set of 2D Gabor filters is designed to extract phase information. The effectiveness of the algorithms is demonstrated on a database of 200 subjects. We also propose a method to synthesize images with novel illumination conditions. This method can be used to generate images to test the illumination-invariant recognition algorithm. The proposed method first estimates the illumination effects in an image through filtering. Next, an illumination-normalized image is extracted to represent a subject. Lastly, the normalized representation and the estimated illumination effects are combined to synthesize new images of the subject under the estimated illumination conditions.

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