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This paper discussed the crucial demand regarding the scheme to translate the silence voice from the newborn. The infant can't afford to express their feeling of pain by voice. Hence, we proudly present an infant pain recognition system to overcome this matter. We employed Saliency Using Natural statistics (SUN) Saliency Map as the feature extraction. We determine the condition of the infants (pain/no pain) with Support Vector Machine (SVM) Classifier. Several examples were conducted to evaluate the performance of the proposed method. [ABSTRACT FROM PUBLISHER]

Most of infant pain cause changes in the face. Clinicians use image analysis to characterize the pathological faces. Nowadays, infant pain research is increasing dramatically due to high demand from all medical team. This paper presents a sparse and naïve Bayes classifier for the diagnosis of infant pain disorders. Phase congruency image and local binary pattern are proposed. The proposed algorithms provide very promising classification rate. [ABSTRACT FROM PUBLISHER]

This paper discussed the crucial demand regarding the scheme to translate the silence voice from the newborn. The infant can't afford to express their feeling of pain by voice. Hence, we proudly present an infant pain recognition system to overcome this matter. We employed the Single Scale Retinex (SSR) to remove the illumination level. Secondly, Gray-Level Co-occurrence Matrix (GLCM) was adopted as the feature extraction. We determine the condition of the infants (pain/no pain) with Hybrid Genetic Algorithm Neural Network (GANN) and Linear Discriminant Analysis (LDA). Several examples were conducted to evaluate the performance of the proposed method under different illumination levels. [ABSTRACT FROM PUBLISHER]

In the last recent years, non-invasive methods through image analysis of facial have been proved to be excellent and reliable tool to diagnose of pain recognition. This paper proposes a new feature vector based Local Binary Pattern (LBP) for the pain detection. Different sampling point and radius weighted are proposed to distinguishing performance of the proposed features. In this work, Infant COPE database is used with illumination added. Multi Scale Retinex (MSR) is applied to remove the shadow. Two different supervised classifiers such as Gaussian and Nearest Mean Classifier are employed for testing the proposed features. The experimental results uncover that the proposed features give very promising classification accuracy of 90% for Infant COPE database. [ABSTRACT FROM PUBLISHER]