STATIC FACE DETECTION AND EMOTION RECOGNITION WITH FPGA SUPPORT

Face detection and analysis systems has been growing in last few years for various applications. Since the hardware performance increase in last few years, useof Deep Learning, Convolution Neural Network, Face detection, Face analysis techniques is increasing and day by day developed models are breaking accuracies of previous models and research in various tasks. Facial analysis system with age, gender and emotion recognition have been proposed with good accuracies for real-time and non-real time both. The present research paper focuses to provide a robust system architecture for age, gender and emotion recognition in real time which can be use in commercial, healthcare, and many more industries. To achieve this a literature survey is done on the same topic with previous researches to compare their results. The final model architecture proposed in this research paper is efficient and fast and provides accurate results as compare to previous researches

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Effective Facial Emotion Recognition using Convolutional Neural Network Algorithm

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d8275.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 4351-4354

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Emotion Recognition ◽

Face Detection ◽

Vital Role ◽

Facial Emotion Recognition ◽

Facial Emotion ◽

Contact Frequency ◽

Static Images ◽

Neural Network Algorithm

This paper presents the idea related to automated live facial emotion recognition through image processing and artificial intelligence (AI) techniques. It is a challenging task for a computer vision to recognize as same as humans through AI. Face detection plays a vital role in emotion recognition. Emotions are classified as happy, sad, disgust, angry, neutral, fear, and surprise. Other aspects such as speech, eye contact, frequency of the voice, and heartbeat are considered. Nowadays face recognition is more efficient and used for many real-time applications due to security purposes. We detect emotion by scanning (static) images or with the (dynamic) recording. Features extracting can be done like eyes, nose, and mouth for face detection. The convolutional neural network (CNN) algorithm follows steps as max-pooling (maximum feature extraction) and flattening.

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Human Emotion Recognition Based on Face and Facial Expression Detection Using Deep Belief Network Under Complicated Backgrounds

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420560108 ◽

2020 ◽

Vol 34 (14) ◽

pp. 2056010

Author(s):

Lei Huang ◽

Fei Xie ◽

Jing Zhao ◽

Shibin Shen ◽

Weiran Guang ◽

...

Keyword(s):

Facial Expression ◽

Emotion Recognition ◽

Face Detection ◽

Skin Color ◽

Facial Expression Recognition ◽

Expression Recognition ◽

Human Face ◽

Human Emotion ◽

The Face ◽

Color Enhancement

The human emotion recognition based on facial expression has a significant meaning in the application of intelligent man–machine interaction. However, the human face images vary largely in real environments due to the complex backgrounds and luminance. To solve this problem, this paper proposes a robust face detection method based on skin color enhancement model and a facial expression recognition algorithm with block principal component analysis (PCA). First, the luminance range of human face image is broadened and the contrast ratio of skin color is strengthened by the homomorphic filter. Second, the skin color enhancement model is established using YCbCr color space components to locate the face area. Third, the feature based on differential horizontal integral projection is extracted from the face. Finally, the block PCA with deep neural network is used to accomplish the facial expression recognition. The experimental results indicate that in the case of weaker illumination and more complicated backgrounds, both the face detection and facial expression recognition can be achieved effectively by the proposed algorithm, meanwhile the mean recognition rate obtained by the facial expression recognition method is improved by 2.7% comparing with the traditional Local Binary Patterns (LBPs) method.

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