M Vignesh M Vignesh
Biographic Data
| ID | 10132002 |
|---|---|
| NAME | M Vignesh M Vignesh |
| GIVEN NAMES | M Vignesh M |
| FAMILY NAME | Vignesh |
| SIGNATURE | VIGNESH M V M |
| VERIFIED | No |
| TOTAL WORKS | 1 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 1 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Deep Learning Approaches to Emotion Recognition in Photographic Images
Photo Emotion Recognition (PER) is supposed to learn what emotion is expressed or invoked by an image based on visual representations of color harmony, composition, object-scene semantics, human expressions in the presence when possible. In contrast to face-centric affect analysis, PER needs to analyze the emotions that frequently are a result of situational semantics and aesthetics, as opposed to explicit facial expression. This enhances ambigui…
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Deep Learning Approaches to Emotion Recognition in Photographic Images
Photo Emotion Recognition (PER) is supposed to learn what emotion is expressed or invoked by an image based on visual representations of color harmony, composition, object-scene semantics, human expressions in the presence when possible. In contrast to face-centric affect analysis, PER needs to analyze the emotions that frequently are a result of situational semantics and aesthetics, as opposed to explicit facial expression. This enhances ambigui…
Convolutional neural network (1 work) · Deep learning (1 work) · Disgust (1 work) · Emotion and Mood Recognition (1 work) · Emotion classification (1 work) · Sadness (1 work) · Salient (1 work) · Sentiment Analysis and Opinion Mining (1 work) · Visual Attention and Saliency Detection (1 work)