What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Facial recognition system, Face, Computer vision and Pattern recognition.
His Feature extraction, Convolutional neural network and Biometrics study in the realm of Artificial intelligence interacts with subjects such as Crowdsourcing and Spoofing attack.
His study deals with a combination of Facial recognition system and Expression.
His study looks at the relationship between Face and topics such as Feature, which overlap with Support vector machine, User authentication, Database, Face Presentation and Texture.
His work on Feature and Preprocessor as part of general Computer vision research is frequently linked to Sketch and Reflectivity, bridging the gap between disciplines.
His Pattern recognition research is multidisciplinary, relying on both Artificial neural network, Object detection, Representation and Frame difference.
His most cited work include:
- Face Spoof Detection With Image Distortion Analysis (397 citations)
- Demographic Estimation from Face Images: Human vs. Machine Performance (233 citations)
- Secure Face Unlock: Spoof Detection on Smartphones (184 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Pattern recognition, Facial recognition system, Face and Computer vision.
His study in Convolutional neural network, Feature extraction, Image, Feature and Robustness is done as part of Artificial intelligence.
His work in Pattern recognition tackles topics such as Deep learning which are related to areas like Volume.
His Facial recognition system research includes elements of RGB color model, Speech recognition and Preprocessor.
The study incorporates disciplines such as Representation and Feature learning in addition to Face.
His study looks at the relationship between Computer vision and fields such as Identification, as well as how they intersect with chemical problems.
He most often published in these fields:
- Artificial intelligence (84.37%)
- Pattern recognition (46.88%)
- Facial recognition system (36.46%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (84.37%)
- Pattern recognition (46.88%)
- Face (36.46%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Pattern recognition, Face, Image and Computer vision.
His study on Feature, Facial recognition system and Segmentation is often connected to SIGNAL as part of broader study in Artificial intelligence.
His work carried out in the field of Facial recognition system brings together such families of science as Discriminative model and Training set.
His research integrates issues of Region of interest, Deep learning, Robustness and Anticipation in his study of Pattern recognition.
Face is closely attributed to Communication channel in his research.
His study focuses on the intersection of Computer vision and fields such as Identification with connections in the field of Perspective, Biometrics and DICOM.
Between 2019 and 2021, his most popular works were:
- RhythmNet: End-to-End Heart Rate Estimation From Face via Spatial-Temporal Representation (28 citations)
- Cross-Domain Face Presentation Attack Detection via Multi-Domain Disentangled Representation Learning (15 citations)
- FCSR-GAN: Joint Face Completion and Super-Resolution via Multi-Task Learning (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Face, Pattern recognition, Computer vision and Image.
Hu Han performs multidisciplinary study in Artificial intelligence and Multi-task learning in his work.
His Face research is multidisciplinary, incorporating perspectives in Pixel, Identification and Code.
His research links Facial recognition system with Pattern recognition.
Hu Han interconnects End-to-end principle and Representation in the investigation of issues within Computer vision.
His studies in Image integrate themes in fields like Segmentation, Landmark, Divergence and Convolutional neural network.
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