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Journal ArticleDOI

Deep Appearance Model and Crow-Sine Cosine Algorithm-Based Deep Belief Network for Age Estimation

01 Jul 2021-International Journal of Ambient Computing and Intelligence (IGI Global)-Vol. 12, Iss: 3, pp 185-207
TL;DR: The overall process of age estimation is performed using three important steps, where the DBN classifier is trained optimally using the proposed learning algorithm named as crow-sine cosine algorithm (CS).
Abstract: Age estimation has been paid great attention in the field of intelligent surveillance, face recognition, biometrics, etc. In contrast to other facial variations, aging variation presents several unique characteristics, which make age estimation very challenging. The overall process of age estimation is performed using three important steps. In the first step, the pre-processing is performed from the input image based on Viola-Jones algorithm to detect the face region. In the second step, feature extraction is done based on three important features such as local transform directional pattern (LTDP), active appearance model (AAM), and the new feature, deep appearance model (Deep AM). After feature extraction, the classification is carried out based on the extracted features using deep belief network (DBN), where the DBN classifier is trained optimally using the proposed learning algorithm named as crow-sine cosine algorithm (CS).
References
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Journal ArticleDOI
Abstract: We describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors.

6,025 citations


7


Journal ArticleDOI
TL;DR: The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces.
Abstract: This paper proposes a novel population-based optimization algorithm called Sine Cosine Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions. Several random and adaptive variables also are integrated to this algorithm to emphasize exploration and exploitation of the search space in different milestones of optimization. The performance of SCA is benchmarked in three test phases. Firstly, a set of well-known test cases including unimodal, multi-modal, and composite functions are employed to test exploration, exploitation, local optima avoidance, and convergence of SCA. Secondly, several performance metrics (search history, trajectory, average fitness of solutions, and the best solution during optimization) are used to qualitatively observe and confirm the performance of SCA on shifted two-dimensional test functions. Finally, the cross-section of an aircraft's wing is optimized by SCA as a real challenging case study to verify and demonstrate the performance of this algorithm in practice. The results of test functions and performance metrics prove that the algorithm proposed is able to explore different regions of a search space, avoid local optima, converge towards the global optimum, and exploit promising regions of a search space during optimization effectively. The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces. Note that the source codes of the SCA algorithm are publicly available at http://www.alimirjalili.com/SCA.html .

1,571 citations


Journal ArticleDOI
TL;DR: Simulation results reveal that using CSA may lead to finding promising results compared to the other algorithms, and this paper proposes a novel metaheuristic optimizer, named crow search algorithm (CSA), based on the intelligent behavior of crows.
Abstract: This paper proposes a novel metaheuristic optimizer, named crow search algorithm (CSA), based on the intelligent behavior of crows. CSA is a population-based technique which works based on this idea that crows store their excess food in hiding places and retrieve it when the food is needed. CSA is applied to optimize six constrained engineering design problems which have different natures of objective functions, constraints and decision variables. The results obtained by CSA are compared with the results of various algorithms. Simulation results reveal that using CSA may lead to finding promising results compared to the other algorithms.

940 citations


Journal ArticleDOI
01 Feb 2004-
TL;DR: The aim of this work is to design classifiers that accept the model-based representation of unseen images and produce an estimate of the age of the person in the corresponding face image, which indicates that machines can estimate theAge of a person almost as reliably as humans.
Abstract: We describe a quantitative evaluation of the performance of different classifiers in the task of automatic age estimation. In this context, we generate a statistical model of facial appearance, which is subsequently used as the basis for obtaining a compact parametric description of face images. The aim of our work is to design classifiers that accept the model-based representation of unseen images and produce an estimate of the age of the person in the corresponding face image. For this application, we have tested different classifiers: a classifier based on the use of quadratic functions for modeling the relationship between face model parameters and age, a shortest distance classifier, and artificial neural network based classifiers. We also describe variations to the basic method where we use age-specific and/or appearance specific age estimation methods. In this context, we use age estimation classifiers for each age group and/or classifiers for different clusters of subjects within our training set. In those cases, part of the classification procedure is devoted to choosing the most appropriate classifier for the subject/age range in question, so that more accurate age estimates can be obtained. We also present comparative results concerning the performance of humans and computers in the task of age estimation. Our results indicate that machines can estimate the age of a person almost as reliably as humans.

585 citations


7


Journal ArticleDOI
TL;DR: It is shown that defining edges in this manner causes some obvious edges to be missed and how to revise the Canny edge detector to improve its detection accuracy is shown.
Abstract: The Canny edge detector is widely used in computer vision to locate sharp intensity changes and to find object boundaries in an image. The Canny edge detector classifies a pixel as an edge if the gradient magnitude of the pixel is larger than those of pixels at both its sides in the direction of maximum intensity change. In this paper we will show that defining edges in this manner causes some obvious edges to be missed. We will also show how to revise the Canny edge detector to improve its detection accuracy.

446 citations