Reconstruction of images from Gabor graphs with applications in facial image processing

Graphs labeled with complex-valued Gabor jets are one of the important data formats for face recognition and the classification of facial images into medically relevant classes like genetic syndromes. We here present an interpolation rule and an iterative algorithm for the reconstruction of images from these graphs. This is especially important if graphs have been manipulated for information processing. One such manipulation is averaging the graphs of a single syndrome, another one building a composite face from the features of various individuals. In reconstructions of averaged graphs of genetic syndromes, the patients' identities are suppressed, while the properties of the syndromes are emphasized. These reconstructions from average graphs have a much better quality than averaged images.

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The Effect of Feature Displacement on Face Recognition

Perception ◽

10.1068/p130505 ◽

1984 ◽

Vol 13 (5) ◽

pp. 505-512 ◽

Cited By ~ 123

Author(s):

Nigel D Haig

Keyword(s):

Image Processing ◽

Face Recognition ◽

Visual Recognition ◽

High Sensitivity ◽

Human Beings ◽

Monitor Screen ◽

High Significance ◽

Facial Images ◽

Vertical Positioning ◽

Very High

Human beings possess a remarkable ability to recognise familiar faces quickly and without apparent effort. In spite of this facility, the mechanisms of visual recognition remain tantalisingly obscure. An experiment is reported in which image processing equipment was used to displace slightly the features of a set of original facial images to form groups of modified images. Observers were then required to indicate whether they were being shown the “original” or a “modified” face, when shown one face at a time on a TV monitor screen. Memory reinforcement was provided by displaying the original face at another screen position, between presentations. The data show, inter alia, the very high significance of the vertical positioning of the mouth, followed by eyes, and then the nose, as well as high sensitivity to close-set eyes, coupled with marked insensitivity to wide-set eyes. Implications of the results for the use of recognition aids such as Identikit and Photofit are briefly discussed.

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Pengenalan Wajah Menggunakan PCA dengan Memperhatikan Jumlah Data Latih dan Vektor Eigen

Jurnal Informatika Universitas Pamulang ◽

10.32493/informatika.v6i1.7261 ◽

2021 ◽

Vol 6 (1) ◽

pp. 1

Author(s):

Rifki Kosasih

Keyword(s):

Face Recognition ◽

Test Data ◽

Principle Component Analysis ◽

Image Data ◽

Training Data ◽

Facial Image ◽

Accuracy Rate ◽

Principle Component ◽

Pca Method ◽

Facial Images

To find out if an employee is present, attendance is usually used. Attendance can be done in several ways, one of which is by filling in the attendance list that has been provided (manual attendance). However, this method is less effective because there is a possibility that employees who are not present will entrust attendance to employees who are present. Therefore, other ways are needed so that this does not happen. In this study, attendance was carried out using facial recognition. Face recognition is one of the fields used to recognize someone. A person's face usually has special characteristics that are easily recognized by people. These special characteristics are also called features. In this study, these features can be searched using the Principle Component Analysis (PCA) method. The PCA method is one of the methods used to produce features by reducing dimensions using eigenvectors from facial images (eigenface). The facial image used in this study consisted of 40 people with each person having 10 facial images with various expressions. Image data is divided into two parts, namely training data and test data. In this study, it is proposed to pay attention to the amount of training data and the number of eigenvectors used to get the best level of accuracy. From the research results, the highest level of accuracy occurs when the training data for each person is 7 and the test data for each person is 3 with an accuracy rate of 96.67%.

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PROJECTIVE NON-NEGATIVE MATRIX FACTORIZATION WITH APPLICATIONS TO FACIAL IMAGE PROCESSING

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001407005983 ◽

2007 ◽

Vol 21 (08) ◽

pp. 1353-1362 ◽

Cited By ~ 34

Author(s):

ZHIRONG YANG ◽

ZHIJIAN YUAN ◽

JORMA LAAKSONEN

Keyword(s):

Image Processing ◽

Matrix Factorization ◽

Recognition Rate ◽

Objective Functions ◽

Facial Image ◽

Semantic Classes ◽

Straight Line ◽

New Variant ◽

Facial Images ◽

Non Negative Matrix Factorization

We propose a new variant of Non-negative Matrix Factorization (NMF), including its model and two optimization rules. Our method is based on positively constrained projections and is related to the conventional SVD or PCA decomposition. The new model can potentially be applied to image compression and feature extraction problems. Of the latter, we consider processing of facial images, where each image consists of several parts and for each part the observations with different lighting mainly distribute along a straight line through the origin. No regularization terms are required in the objective functions and both suggested optimization rules can easily be implemented by matrix manipulations. The experiments show that the derived base vectors are spatially more localized than those of NMF. In turn, the better part-based representations improve the recognition rate of semantic classes such as the gender or existence of mustache in the facial images.

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Partially Occluded Facial Image Retrieval Based on a Similarity Measurement

Mathematical Problems in Engineering ◽

10.1155/2015/217568 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

Sohee Park ◽

Hansung Lee ◽

Jang-Hee Yoo ◽

Geonwoo Kim ◽

Soonja Kim

Keyword(s):

Face Recognition ◽

Image Retrieval ◽

Similarity Measurement ◽

Facial Image ◽

Scale Invariant ◽

Recognition Algorithms ◽

Benchmark Database ◽

Histogram Intersection ◽

Partially Occluded ◽

Facial Images

We present a partially occluded facial image retrieval method based on a similarity measurement for forensic applications. The main novelty of this method compared with other occluded face recognition algorithms is measuring the similarity based on Scale Invariant Feature Transform (SIFT) matching between normal gallery images and occluded probe images. The proposed method consists of four steps: (i) a Self-Quotient Image (SQI) is applied to input images, (ii) Gabor-Local Binary Pattern (Gabor-LBP) histogram features are extracted from the SQI images, (iii) the similarity between two compared images is measured by using the SIFT matching algorithm, and (iv) histogram intersection is performed on the SIFT-based similarity measurement. In experiments, we have successfully evaluated the performance of the proposed method with the commonly used benchmark database, including occluded facial images. The results show that the correct retrieval ratio was 94.07% in sunglasses occlusion and 93.33% in scarf occlusion. As such, the proposed method achieved better performance than other Gabor-LBP histogram-based face recognition algorithms in eyes-hidden occlusion of facial images.

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High-precision Classification of Small Insects with Similar Color and Shape Using Image Processing

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.136.1120 ◽

2016 ◽

Vol 136 (8) ◽

pp. 1120-1127 ◽

Cited By ~ 1

Author(s):

Naoya Ikemoto ◽

Kenji Terada ◽

Yuta Takashina ◽

Akio Nakano

Keyword(s):

Image Processing ◽

High Precision

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An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0107 ◽

2017 ◽

Vol 7 (7) ◽

pp. 408

Author(s):

Yashpal Jitarwal ◽

Tabrej Ahamad Khan ◽

Pawan Mangal

Keyword(s):

Image Processing ◽

Texture Features ◽

Automatic Classification ◽

Processing Technique ◽

Image Processing Technique ◽

Advance Technique ◽

Time Required ◽

Fruit Sorting ◽

Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

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