Face Recognition MATLAB Projects - MATLAB Solutions
Explore face detection, lip localization, deep learning-based recognition and related MATLAB project ideas.
Explore face detection, lip localization, deep learning-based recognition and related MATLAB project ideas.
Face recognition and lip localization are two main building blocks in the development of audio visual automatic speech recognition systems (AV-ASR). This project uses infrared and depth images captured by the Kinect V2 device to perform face detection and uses depth information to reduce the lip search area via nose point detection.
An approach to incorporate visual speech information into ASR systems for noisy environments using Gabor filters for robust face detection and lip localization under changing lighting and background clutter.
Algorithms based on a modified HSI color space to locate face, eyes, and lips in visually challenging environments; tested on imagery collected in the wild.
Model perspective distortion as a family of warping functions to improve recognition under small focal lengths; also includes a modular object tracking library useful for vision research.
Study and implement additive classifiers and efficient training/evaluation techniques for object detection and image classification in MATLAB implementations.
An example project demonstrating parallel algorithm implementation and performance analysis strategies in MATLAB and C++—included here to illustrate parallelization patterns that can apply to vision workloads.
Evaluate how recognition performance depends on internal (eyes, nose, mouth) vs external (chin, hairline) face features for individuals with central vision loss.
Implement attention-based deep learning models to emphasize discriminant facial features and improve recognition accuracy (e.g., bilinear models or attention modules).
Integrate RFID authentication with MATLAB-based face recognition to build an automotive security prototype with 24/7 operation capability.
Implement DCT-based feature extraction and Self Organizing Map (SOM) classification for face recognition; includes evaluation on a small dataset and performance analysis in MATLAB.
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In today\\\'s rapidly advancing era of automation, robotics control systems are evolving to meet the demand for smarter, faster, and more reliable performance. Among the many innovations driving this transformation is the use of MCP (Model-based Control Paradigms)
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