Face and Emotion Recognition from Real-Time Facial Expressions Using Deep Learning Algorithms
- Title
- Face and Emotion Recognition from Real-Time Facial Expressions Using Deep Learning Algorithms
- Creator
- Monica S.; Roseline Mary R.
- Description
- Emotions are faster than words in the field of humancomputer interaction. Identifying human facial expressions can be performed by a multimodal approach that includes body language, gestures, speech, and facial expressions. This paper throws light on emotion recognition via facial expressions, as the face is the basic index of expressing our emotions. Though emotions are universal, they have a slight variation from one person to another. Hence, the proposed model first detects the face using histogram of gradients (HOG) recognized by deep learning algorithms such as linear support vector machine (LSVM), and then, the emotion of that person is detected through deep learning techniques to increase the accuracy percentage. The paper also highlights the data collection and preprocessing techniques. Images were collected using a simple HAAR classifier program, resized, and preprocessed by removing noise using a mean filter. The model resulted in an accuracy percentage for face and emotion being 97% and 92%, respectively. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Source
- Lecture Notes on Data Engineering and Communications Technologies, Vol-114, pp. 451-460.
- Date
- 2022-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Deep learning; Emotion recognition; Face recognition; Neural networks
- Coverage
- Monica S., CHRIST (Deemed to be University), Bangalore, India; Roseline Mary R., CHRIST (Deemed to be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 23674512
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Monica S.; Roseline Mary R., “Face and Emotion Recognition from Real-Time Facial Expressions Using Deep Learning Algorithms,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 22, 2025, https://archives.christuniversity.in/items/show/18662.