این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Journal of Artificial Intelligence and Data Mining، جلد ۹، شماره ۲، صفحات ۲۵۹-۲۶۸
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Facial Expression Recognition based on Image Gradient and Deep Convolutional Neural Network
چکیده انگلیسی مقاله Facial Expression Recognition (FER) is one of the basic ways of interacting with machines and has been getting more attention in recent years. In this paper, a novel FER system based on a deep convolutional neural network (DCNN) is presented. Motivated by the powerful ability of DCNN to learn features and image classification, the goal of this research is to design a compatible and discriminative input for pre-trained AlexNet-DCNN. The proposed method consists of 4 steps: first, extracting three channels of the image including the original gray-level image, in addition to horizontal and vertical gradients of the image similar to the red, green, and blue color channels of an RGB image as the DCNN input. Second, data augmentation including scale, rotation, width shift, height shift, zoom, horizontal flip, and vertical flip of the images are prepared in addition to the original images for training the DCNN. Then, the AlexNet-DCNN model is applied to learn high-level features corresponding to different emotion classes. Finally, transfer learning is implemented on the proposed model and the presented model is fine-tuned on target datasets. The average recognition accuracy of 92.41% and 93.66% were achieved for JAFEE and CK+ datasets, respectively. Experimental results on two benchmark emotional datasets show promising performance of the proposed model that can improve the performance of current FER systems.
کلیدواژه‌های انگلیسی مقاله Facial expression recognition, deep convolutional neural network, three-channel of the image, AlexNet DCNN, Transfer learning
نویسندگان مقاله M. R. Fallahzadeh |
Department of Technical and engineering, Central Tehran Branch, Islamic Azad University, Iran.

F. Farokhi |
Department of Technical and engineering, Central Tehran Branch, Islamic Azad University, Iran.

A. Harimi |
Department of Technical and engineering, Shahrood Branch, Islamic Azad University, Iran.

R. Sabbaghi-Nadooshan |
Department of Technical and engineering, Central Tehran Branch, Islamic Azad University, Iran.

نشانی اینترنتی http://jad.shahroodut.ac.ir/article_1984_d6defdaecb37c99a20be0292ea6aef2f.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات