Karim Mokrani
Papers
1
Total Citations
18
H-Index
1
About
Karim Mokrani is a researcher at the forefront of affective computing and computer vision, with a primary focus on advancing facial expression recognition (FER) systems. His work centers on the critical challenge of automatically interpreting human emotional states from facial cues—a technology with vast implications for human-computer interaction, mental health monitoring, and intelligent systems. Mokrani’s most notable contribution, the 2019 paper "Fusion of transformed shallow features for facial expression recognition," has garnered 18 citations and introduces a novel hybrid approach that combines shallow, handcrafted features with transformed representations to improve recognition accuracy. This work addresses the inherent complexity of facial expressions, which convey subtle signals about cognitive activity, intention, and personality. By bridging traditional feature engineering with modern transformation techniques, Mokrani has helped push the boundaries of how machines perceive and respond to human affect. His research continues to inspire new methods in robust, real-world FER applications, making him a rising voice in the intersection of emotion AI and pattern recognition.
Research Focus
Key Achievements
Top Papers
- 1Fusion of transformed shallow features for facial expression recognition18 citations · 2019