Collection of HAAR and LBP cascades designed to recognize various street signs

👨 使用 OpenCV 和 Qt 实现人脸（刷脸）登录

Emotion recognition system based on LBP-TOP.

The program uses HOG and LBP features to detect human in images. First, use the HOG feature only to detect humans. Next, combine the HOG feature with the LBP feature to form an augmented feature (HOG-LBP) to detect human. A Two-Layer Perceptron (feedforward neural network) will be used to classify the input feature vector into human or no-human.

Lab Experiments under Lab component of CSE3018 - Content-based Image and Video Retrieval course at Vellore Institute of Technology, Chennai

Detect, recognize and verify faces using hybrid features: “deep” features from VGG-net + HoG + LBP. Hybrid Features help increase recognition significantly

A module that can extract LBP features (local binary pattern) from 3D images. Can be used for extracting features from medical images.

A SPATIAL AND FREQUENCY BASED METHOD FOR MICRO FACIAL EXPRESSIONS RECOGNITION USING COLOR AND DEPTH IMAGES

A two-class fingerprint spoof detector that uses Local Binary Patterns (LBP) features along with Support Vector Machines (SVM) to distinguish live fingerprints images from spoof samples.

Texture Segmentation using: Gray-Level Co-occurence Matrix, Leung-Malik (LM) Filter Bank and Schmid (S) Filter Bank and Local Binary Pattern.

Facial Expression Classification and Feature Extraction

Where Is (the) Ball /Ball Recognition with OpenCV

texture classification with svm using lbp and glcm

Malaria Detection Project on Malaria Cells

A selection of python scripts regarding face morphing generation and detection

Gender Detection Project

Brain MRI tumor classifier using HOG + LBP feature fusion and a tuned SVM (RBF kernel)

Fingerprint-Spoof-Detector

A face anti-spoofing detection system built using Principal Component Analysis (PCA) for dimensionality reduction and feature extraction. The system classifies facial images as Real (live person) or Fake (printed photo, replay attack, or mask), using the CASIA Face Anti-Spoofing Dataset.