Papers
11
Total Citations
178
H-Index
5
About
Abdulrahman Altahhan is a researcher whose work spans affective computing, deep learning, and autonomous robotics, with a particular focus on enabling machines to perceive and respond to human emotional states. He is perhaps best known for his contributions to facial expression-based emotion recognition, where his development of hybrid deep learning architectures and stacked convolutional autoencoders has significantly advanced the field. His 2018 paper on hybrid deep learning for emotion recognition in socially assistive robots has garnered 83 citations, while his 2017 work on stacked deep convolutional autoencoders has accumulated 54 citations — together reflecting substantial influence on human-machine interaction research. Altahhan's earlier work laid important groundwork in robot visual homing, employing Temporal Difference learning methods, radial basis features, and reinforcement learning frameworks to guide autonomous navigation. His more recent investigations into self-reflective deep reinforcement learning and deep feature-action processing demonstrate a commitment to pushing the boundaries of how robots learn from complex, high-dimensional sensory environments. Across his career, Altahhan has consistently bridged cognitive science-inspired principles with practical machine learning implementations, making his research particularly valuable for students and practitioners working at the intersection of robotics, artificial intelligence, and human-centered computing.
Research Focus
Key Achievements
Top Papers
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- 3Emotion Recognition Using Facial Expression Images for a Robotic Companion11 citations · 2016
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- 7Deep Feature-Action Processing with Mixture of Updates4 citations · 2015
- 8Self-reflective deep reinforcement learning4 citations · 2016
- 9Navigating a Robot through Big Visual Sensory Data3 citations · 2015
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