Efficient Iris and Eyelids Detection from Facial Sketch Images

Abstract

In this paper, we propose a simple yet effective technique for an automatic iris and eyelids detection method for facial sketch images. Our system uses Circular Hough Transformation (CHT) algorithm for iris localization process and a low level grayscale analysis for eyelids contour segmentation procedure. We limit the input face for the system to facial sketch photos with frontal pose, illumination invariant, neutral expression and without occlusions. CUHK and IIIT-D sketch databases are used to acquire the experimental results. As to validate the proposed algorithm, experiments on ground truth for iris and eyelids segmentation, which are prepared at our lab, is conducted. The iris segmentation from the proposed method gives the best accuracy of 92.93 and 86.71 based on F-measure evaluation for IIIT-D and CUHK, respectively. For eyelids segmentation, on the other hand, the proposed algorithm achieves an average of 4 standard deviation which indicates the closeness of proposed method to ground truth.

Emotion Detection Using Deep Normalized Attention-Based Neural Network and Modified-Random Forest

“…Furthermore, signal processing approaches and optimized SVM with genetic algorithm have been employed to develop the smart methodology for enhancing emotion detection. Attained outcomes have explored that, the recommended system has revealed 93.86% accuracy [ 13 , 14 ]. A multichannel feature fusion approach has been considered for recognizing varied human emotions.…”

“…Accordingly, an entropy-weighted clustering approach has been integrated with sparse learning has exposed 68.35% [ 8 ], and ANN has shown 84.3%. SVM has achieved 77.1% accuracy [ 10 ], while Rotation Forest-SVM has attained 93.1% [ 19 ], LSTM with attention Autoencoder has achieved 76.7% [ 23 ], optimized SVM reached 93.86% [ 13 ], K-NN reached 83.77%, and ANN reached 84.50% [ 25 ], and CNN has attained 94.13% [ 21 ]. Though better performance has been attained, the accuracy rate has to be enhanced for effective emotion detection [ 11 ].…”