Face Recognition MATLAB Projects - MATLAB Solutions

IR-Depth Face Detection and Lip Localization Using Kinect V2

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.

Robust Unconstrained Face Detection and Lip Localization Using Gabor Filters

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.

Face and Lip Localization in Unconstrained Imagery

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.

Perspective Distortion Modeling in Face Images and Object Tracking Library

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.

Algorithms and Representations for Visual Recognition

Study and implement additive classifiers and efficient training/evaluation techniques for object detection and image classification in MATLAB implementations.

Parallel Modeling of Fish Interaction (methodology example)

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.

The Role of External Features in Face Recognition with Central Vision Loss

Evaluate how recognition performance depends on internal (eyes, nose, mouth) vs external (chin, hairline) face features for individuals with central vision loss.

Attention Modeling for Face Recognition via Deep Learning

Implement attention-based deep learning models to emphasize discriminant facial features and improve recognition accuracy (e.g., bilinear models or attention modules).

Microcontroller-based Automotive Security System using RFID with Face Recognition

Integrate RFID authentication with MATLAB-based face recognition to build an automotive security prototype with 24/7 operation capability.

MATLAB-based Face Recognition System using Image Processing and Neural Networks

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.

Sample MATLAB Implementation: Automated Face Detection

Locating human faces using Viola-Jones cascade detector:

% Create Cascade Face Detector
faceDetector = vision.CascadeObjectDetector();

% Read Input Image
img = imread('pears.png');
bboxes = step(faceDetector, img);

% Annotate Detected Faces
IFaces = insertObjectAnnotation(img, 'rectangle', bboxes, 'Face');
figure; imshow(IFaces); title('Detected Faces');

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