COMPARATIVE ASSESSMENT OF CONTENT-BASED FACE IMAGE RETRIEVAL IN DIFFERENT COLOR SPACES

2005 ◽  
Vol 19 (07) ◽  
pp. 873-893 ◽  
Author(s):  
PEICHUNG SHIH ◽  
CHENGJUN LIU
Keyword(s):  
Face Recognition ◽  
Image Retrieval ◽  
Color Space ◽  
Principal Component ◽  
Face Image ◽  
Color Images ◽  
Grand Challenge ◽  
Color Spaces ◽  
The Face ◽  
Different Color

Content-based face image retrieval is concerned with computer retrieval of face images (of a given subject) based on the geometric or statistical features automatically derived from these images. It is well known that color spaces provide powerful information for image indexing and retrieval by means of color invariants, color histogram, color texture, etc. This paper assesses comparatively the performance of content-based face image retrieval in different color spaces using a standard algorithm, the Principal Component Analysis (PCA), which has become a popular algorithm in the face recognition community. In particular, we comparatively assess 12 color spaces (RGB, HSV, YUV, YCbCr, XYZ, YIQ, L*a*b*, U*V*W*, L*u*v*, I1I2I3, HSI, and rgb) by evaluating seven color configurations for every single color space. A color configuration is defined by an individual or a combination of color component images. Take the RGB color space as an example, possible color configurations are R, G, B, RG, RB, GB and RGB. Experimental results using 600 FERET color images corresponding to 200 subjects and 456 FRGC (Face Recognition Grand Challenge) color images of 152 subjects show that some color configurations, such as YV in the YUV color space and YI in the YIQ color space, help improve face retrieval performance.

Estimation of Fractal Dimension in Different Color Model

2018 ◽  
Vol 8 (1) ◽  
pp. 75-93
Author(s):  
Sumitra Kisan ◽  
Sarojananda Mishra ◽  
Ajay Chawda ◽  
Sanjay Nayak
Keyword(s):  
Fractal Dimension ◽  
Color Space ◽  
Vital Role ◽  
Color Images ◽  
Color Spaces ◽  
Fractal Surfaces ◽  
Test Operation ◽  
Novel Method ◽  
Rgb Color Space ◽  
Different Color

This article describes how the term fractal dimension (FD) plays a vital role in fractal geometry. It is a degree that distinguishes the complexity and the irregularity of fractals, denoting the amount of space filled up. There are many procedures to evaluate the dimension for fractal surfaces, like box count, differential box count, and the improved differential box count method. These methods are basically used for grey scale images. The authors' objective in this article is to estimate the fractal dimension of color images using different color models. The authors have proposed a novel method for the estimation in CMY and HSV color spaces. In order to achieve the result, they performed test operation by taking number of color images in RGB color space. The authors have presented their experimental results and discussed the issues that characterize the approach. At the end, the authors have concluded the article with the analysis of calculated FDs for images with different color space.

scholarly journals A Curvelet Domain Face Recognition Scheme Based on Local Dominant Feature Extraction

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Hafiz Imtiaz ◽  
Shaikh Anowarul Fattah
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Selection Criterion ◽  
Curvelet Transform ◽  
Principal Component ◽  
Face Image ◽  
Spatial Variations ◽  
Threshold Criterion ◽  
Dominant Feature ◽  
The Face

A feature extraction algorithm is introduced for face recognition, which efficiently exploits the local spatial variations in a face image utilizing curvelet transform. Although multi-resolution ideas have been profusely employed for addressing face recognition problems, theoretical studies indicate that digital curvelet transform is an even better method due to its directional properties. Instead of considering the entire face image, an entropy-based local band selection criterion is developed for feature extraction, which selects high-informative horizontal bands from the face image. These bands are segmented into several small spatial modules to capture the local spatial variations precisely. The effect of modularization in terms of the entropy content of the face images has been investigated. Dominant curvelet transform coefficients corresponding to each local region residing inside the horizontal bands are selected, based on the proposed threshold criterion, as features, which not only drastically reduces the feature dimension but also provides high within-class compactness and high between-class separability. A principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentation is carried out upon standard face databases and a very high degree of recognition accuracy is achieved even with a simple Euclidean distance based classifier.

scholarly journals Aplikasi Screen Lock pada Smartphone Menggunakan Identifikasi Wajah dengan Menerapkan Pointwise

2015 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Andi Widiyanto ◽  
Bintang Dian Mahardika
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Success Rate ◽  
Face Image ◽  
Color Images ◽  
Face Identification ◽  
Test Results ◽  
Image Capture ◽  
The Face ◽  
True Color

Penerapan identifikasi wajah (face recognition) telah diterapkan pada komputer, laptop atau alat-alat lain yang memang dikhususkan untuk identifikasi wajah. Perkembangan smartphone khususnya android berkembang dengan cepat. Untuk menjaga keamanan supaya hanya dapat digunakan oleh pemilik telah disediakan dengan PIN, phone code, pola geser titik sentuh layar. Aplikasi identifikasi wajah digunakan sebagai pengganti PIN atau code phone pada smartphone android dibutuhkan sebagai proteksi supaya hanya pemiliknya saja yang dapat menggunakannya. Supaya proses identifikasi wajah pemilik lebih mudah perlu dilakukan konversi dari gambar true color ke grayscale proses yang digunakan adalah pointwise. Aplikasi face recognition yang dibangun membutuhkan training wajah pemilik dengan 6 pose wajah yang disimpan, kemudian akan dibandingkan dengan identifikasi wajah saat aplikasi digunakan. Hasil pengujian menunjukkan bahwa tingkat keberhasilan antara 70% - 90%. Jarak antara wajah dan kamera serta tingkat kecerahan cahaya mempengaruhi hasil dari identifikasi wajah. Tingkat keberhasilan identifikasi wajah ditentukan oleh pengambilan image, pemrosesan image, dan perhitungan dengan PCA (eigenface).Face recognition has been implemented on a computer, laptop or other device tool which is dedicated for face identification. Developments in particular android smartphones growing rapidly. To maintain the security that can only be used by owners have been provided with a PIN, phone code, pattern shear point touch screen. Face recognition application used as a substitute for or a PIN code on the phone android smartphone needed as protection so only the owner who can use it. So that the process of identification of the owner's face needs to be done easier conversion of true color images into grayscale process used is pointwise. Face recognition application that is built requires owners face training with 6 face pose saved , then will be compared with the face identification when the application is used . The test results showed that the success rate of between 70 % - 90 %. The distance between the face and the camera and the brightness of light affect the results of face identification. The success rate is determined by identifying the face image capture, image processing, and computation with PCA eigenface.

The Research of Face Recognition Method Based on Wavelet Transform

2012 ◽  
Vol 442 ◽  
pp. 463-467
Author(s):  
Li Hong Bi ◽  
Yan Fang Ma ◽  
Li Hua Piao
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Principal Component ◽  
Face Image ◽  
Biometric Identification ◽  
Recognition Method ◽  
Image Block ◽  
The Face ◽  
Speed And Accuracy ◽  
Great Development

Face recognition is a kind of biometric identification technology possessing great development potential, researching on this technology has great theoretical values. The paper presents a method of image block principal component analysis (PCA) based on wavelet transform. The image was firstly disposed by wavelet transform and segmented, then we set the different weight values for each of parts according to the different role in the overall image and extract eigenvector using the PCA, finally, the face image is recognized according to the eigenvector and feature. This method can improve the speed and accuracy, reduce the complexity of feature extraction and improve the speed of recognition.

3D Face Recognition using an Adaptive Non-Uniform Face Mesh

2011 ◽  
pp. 562-573
Author(s):  
Wei Jen Chew ◽  
Kah Phooi Seng ◽  
Li-Minn Ang
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Face Image ◽  
Uniform Mesh ◽  
Range Image ◽  
3D Face Recognition ◽  
Linear Discriminant ◽  
3D Face ◽  
The Face

Face recognition using 3D faces has become widely popular in the last few years due to its ability to overcome recognition problems encountered by 2D images. An important aspect to a 3D face recognition system is how to represent the 3D face image. In this chapter, it is proposed that the 3D face image be represented using adaptive non-uniform meshes which conform to the original range image. Basically, the range image is converted to meshes using the plane fitting method. Instead of using a mesh with uniform sized triangles, an adaptive non-uniform mesh was used instead to reduce the amount of points needed to represent the face. This is because some parts of the face have more contours than others, hence requires a finer mesh. The mesh created is then used for face recognition purposes, using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Simulation results show that an adaptive non-uniform mesh is able to produce almost similar recognition rates compared to uniform meshes but with significant reduction in number of vertices.

scholarly journals FACE RECOGNITION THROUGH CASCADE CLASSIFIER USING EIGENFACES ALGORITHMS.

2020 ◽  
Vol 9 (6) ◽  
pp. 39-47
Keyword(s):  
Face Recognition ◽  
Dimensional Space ◽  
Principal Component ◽  
Face Image ◽  
Human Face ◽  
Cascade Classifier ◽  
Facial Image ◽  
Human Face Recognition ◽  
Cascade Classifiers ◽  
The Face

The easiest way to distinguish each person's identity is through the face. Face recognition is included as an inevitable pre-processing step for face recognition. Face recognition itself has to face difficulties and challenges because sometimes some form of issue is quite different from human face recognition. There are two stages used for the human face recognition process, i.e. face detection, where this process is very fast in humans. In the first phase, the person stored the face image in the database from a different angle. The person's face image storage with the help of Eigenvector value depended on components - face coordinates, face index, face angles, eyes, nose, lips, and mouth within certain distances and positions with each other. There are two types of methods that are popular in currently developed face recognition patterns, the Cascade Classifier method and the Eigenface Algorithm. Facial image recognition The Eigenface method is based on the lack of dimensional space of the face, using principal component analysis for facial features. The main purpose of the use of cascade classifiers on facial recognition using the Eigenface Algorithm was made by finding the eigenvectors corresponding to the largest eigenvalues of the facial image

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