What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Richa Singh focuses on Artificial intelligence, Computer vision, Facial recognition system, Pattern recognition and Face.
Her research in Artificial intelligence focuses on subjects like Machine learning, which are connected to Classifier.
In her research, Statistical classification and Ocular biometrics is intimately related to Identification, which falls under the overarching field of Computer vision.
Her work on Three-dimensional face recognition as part of her general Facial recognition system study is frequently connected to Gabor transform, thereby bridging the divide between different branches of science.
Richa Singh has researched Pattern recognition in several fields, including Image resolution, Feature, Matching, Algorithm design and RGB color model.
The study incorporates disciplines such as Support vector machine, Matching, Identity, Information retrieval and Replay attack in addition to Face.
Her most cited work include:
- A Robust Skin Color Based Face Detection Algorithm (259 citations)
- Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing (236 citations)
- Computationally Efficient Face Spoofing Detection with Motion Magnification (153 citations)
What are the main themes of her work throughout her whole career to date?
Richa Singh mainly investigates Artificial intelligence, Facial recognition system, Pattern recognition, Computer vision and Face.
Her research integrates issues of Matching and Machine learning in her study of Artificial intelligence.
Her Facial recognition system research is multidisciplinary, incorporating elements of Artificial neural network, Feature and Identification.
She regularly links together related areas like Image quality in her Pattern recognition studies.
The Computer vision study combines topics in areas such as Sketch and Authentication.
Her Face research integrates issues from Matching, Image, Identity and Image resolution.
She most often published in these fields:
- Artificial intelligence (77.43%)
- Facial recognition system (42.52%)
- Pattern recognition (38.32%)
What were the highlights of her more recent work (between 2017-2021)?
- Artificial intelligence (77.43%)
- Deep learning (19.95%)
- Facial recognition system (42.52%)
In recent papers she was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Deep learning, Facial recognition system, Machine learning and Pattern recognition.
The study of Artificial intelligence is intertwined with the study of Computer vision in a number of ways.
Her Deep learning research is multidisciplinary, relying on both Adversarial system, Image, Feature learning and Robustness.
Profiling is closely connected to Identification in her research, which is encompassed under the umbrella topic of Facial recognition system.
When carried out as part of a general Machine learning research project, her work on Artificial neural network and Feature is frequently linked to work in Task analysis, therefore connecting diverse disciplines of study.
Her biological study spans a wide range of topics, including Function and Image quality.
Between 2017 and 2021, her most popular works were:
- Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks (62 citations)
- Disguised Faces in the Wild (50 citations)
- Detecting and Mitigating Adversarial Perturbations for Robust Face Recognition (47 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Richa Singh mostly deals with Artificial intelligence, Deep learning, Facial recognition system, Pattern recognition and Face.
Her Artificial intelligence study incorporates themes from Machine learning and Computer vision.
Her studies in Deep learning integrate themes in fields like Iris recognition, Biometrics, Blockchain, Public-key cryptography and Feature extraction.
Her Facial recognition system research is multidisciplinary, incorporating perspectives in Algorithm, Feature learning, Convolutional neural network and Word error rate.
Richa Singh interconnects Autoencoder, Minutiae and Generative grammar in the investigation of issues within Pattern recognition.
Her Face research incorporates elements of Visualization, Pattern recognition, Identity and Identification.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
