Differential Evolution to Optimize Hidden Markov Models Training: Application to Facial Expression Recognition

The base system in this paper uses Hidden Markov Models (HMMs) to model dynamic relationships among facial features in facial behavior interpretation and understanding field. The input of HMMs is a new set of derived features from geometrical distances obtained from detected and automatically tracked facial points. Numerical data representation which is in the form of multi-time series is transformed to a symbolic representation in order to reduce dimensionality, extract the most pertinent information and give a meaningful representation to humans. The main problem of the use of HMMs is that the training is generally trapped in local minima, so we used the Differential Evolution (DE) algorithm to offer more diversity and so limit as much as possible the occurrence of stagnation. For this reason, this paper proposes to enhance HMM learning abilities by the use of DE as an optimization tool, instead of the classical Baum and Welch algorithm. Obtained results are compared against the traditional learning approach and significant improvements have been obtained.</p

A MULTI-AGENT APPROACH FOR EDGE DETECTION USING A GENETIC ALGORITHM FOR PARAMETERS’ SPACE EXPLORATION

In  this  paper,  an  agent  based  approach  for  edge  detection  is  presented.  It  uses  the  blackboard  system  as  a  means  of communication between agents. A population of agents is deployed on a two-dimensional representation of an image. Every agent is able to decide whether the pixel on which it is situated belongs or does not belong to the homogeneous region looked for,  and  thus  to  exhibit  a  reactive  behaviour:  breeding  and  labelling,  or  diffusion,  allowing  the  emergence  of  a  complex phenomenon at the global level. This phenomenon is the segmentation of the image. The behaviour of agents is inspired from the natural diffusion phenomenon. The approach has been implemented with the Netlogo platform which  is a very powerful agent based simulator, and since the parameters space is very huge, a genetic algorithm has been used to lessen the complexity of the problem

Intelligent Content-Based Dermoscopic Image Retrieval with Relevance Feedback for Computer-Aided Melanoma Diagnosis

The use of Computer-Aided Diagnosis in dermatology raises the necessity of integrating Content-Based Image Retrieval (CBIR) technologies. The latter could be helpful to untrained users as a decision support system for skin lesion diagnosis. However, classical CBIR systems perform poorly due to semantic gap. To alleviate this problem, we propose in this paper an intelligent Content-Based Dermoscopic Image Retrieval (CBDIR) system with Relevance Feedback (RF) for melanoma diagnosis that exhibits: efficient and accurate image retrieval as well as visual features extraction that is independent of any specific diagnostic method. After submitting a query image, the proposed system uses linear kernel-based active SVM, combined with histogram intersection-based similarity measure to retrieve the K most similar skin lesion images. The dominant (melanoma, benign) class in this set will be identified as the image query diagnosis. Extensive experiments conducted on our system using a 1097 image database show that the proposed scheme is more effective than CBDIR without the assistance of RF.10

An efficient feature selection scheme based on genetic algorithm for ear biometrics authentication

Human ear recognition is a new biometric technology which competes with other powerful biometrics modalities such as fingerprint, face and iris. Ear has small size, a uniform distribution color and does not need much collaboration from the user. Feature extraction is a crucial stage for biometric identification. However, the extracted features might contain redundant and irrelevant features which can lead to the problem of dimension and even to degradation of performances of biometric systems. In this paper, we present a new efficient feature selection scheme based on Genetic Algorithm for ear biometrics. The proposed approach has been tested on an ear biometrics database and compared with the full feature system, Principal Components Analysis (PCA) based approach and a combination of the proposed GA and PCA. Experimental results show that the proposed approach outperforms the full-feature based system in terms of accuracy, FRR and FAR