Book ChapterDOI
Recognizing Altered Facial Appearances Due to Aging and Disguise
Richa Singh
- pp 77-106
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TLDR
The proposed mutual information based age transformation algorithm registers the gallery and probe face images and minimizes the variations in facial features caused due to aging and the results show that the performance of the proposed algorithm is significantly better than existing algorithms.Abstract:
This chapter focuses on recognizing faces with variations in aging and disguise. In the proposed approach, mutual information based age transformation algorithm registers the gallery and probe face images and minimizes the variations in facial features caused due to aging. Further, gallery and probe face images are decomposed at different levels of granularity to extract non-disjoint spatial features. At the first level, face granules are generated by applying Gaussian and Laplacian operators to extract features at different resolutions and image properties. The second level of granularity divides the face image into vertical and horizontal regions of different sizes to specifically handle variations in pose and disguise. At the third level of granularity, the face image is partitioned into small grid structures to extract local features. A neural network architecture based 2D log polar Gabor transform is used to extract binary phase information from each of the face granules. Finally, likelihood ratio test statistics based support vector machine classification approach is used to classify the granular information. The proposed algorithm is evaluated on multiple databases comprising of disguised faces of real people, disguised synthetic face images, faces with aging variations, and disguised faces of actors and actresses from movie clips that also have aging variations. These databases cover a comprehensive set of aging and disguise scenarios. The performance of the proposed algorithm is compared with existing algorithms and the results show that the performance of the proposed algorithm is significantly better than existing algorithms.read more
Citations
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Proceedings ArticleDOI
Facial age estimation using hybrid Haar wavelet and color features with Support Vector Regression
TL;DR: The combination of Haar wavelet transform and color moment approaches is utilizes to extract full-informative and influencing feature elements of face image to improve the training step of the age estimation system.
Proceedings ArticleDOI
Facial age estimation under the terms of local latency using weighted local binary pattern and multi-layer perceptron
TL;DR: A new Local Binary Pattern (LBP)-based feature extraction method which is combined with a weighting scheme to assign high weights to general LBP feature elements (parts of facial image without local latency) whereas assigns low weights to the feature elements of facial images which are covered by the local latency.
Proceedings ArticleDOI
Age-based human face image retrieval using zernike moments
TL;DR: A new image retrieval which takes facial image and the age of individual as the queries and retrieves the face image or the most similar face image of that person in the selected age is proposed.
References
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Proceedings ArticleDOI
A Study of Face Recognition as People Age
TL;DR: This paper proposes using the gradient orientation pyramid for face verification as a two-class problem and uses a support vector machine as a classifier and finds that the new descriptor yields a robust and discriminative representation.
Proceedings ArticleDOI
Modeling shape and textural variations in aging faces
TL;DR: The proposed shape transformation model is formulated as a physically-based parametric muscle model that captures the subtle deformations facial features undergo with age and an image gradient based texture transformation function is developed that characterizes facial wrinkles and other skin artifacts often observed during different ages.
Proceedings ArticleDOI
A Multi-Resolution Dynamic Model for Face Aging Simulation
TL;DR: A dynamic model for simulating face aging process that represents all face images by a multi-layer and-or graph and integrates three most prominent aspects related to aging changes: global appearance changes in hair style and shape, deformations and aging effects of facial components, and wrinkles appearance at various facial zones is presented.
Journal ArticleDOI
Face recognition with disguise and single gallery images
TL;DR: This paper presents a face recognition algorithm that addresses two major challenges: when an individual intentionally alters the appearance and features using disguises, and when limited gallery images are available for recognition.
Proceedings ArticleDOI
Facial similarity across age, disguise, illumination and pose
TL;DR: The similarity measure helps in studying the significance facial features play in affecting the performance of face recognition systems and proposes a framework to compensate for pose variations and introduces the notion of 'half-faces' to circumvent the problem of non-uniform illumination.