Face_Recogniton Using python-cv2
Please First Watch This Video To Solve This error: (-215:Assertion failed) !empty() in function 'cv::CascadeClassifier::detectMultiScale
https://youtu.be/mNJ2BzTRDQw?si=_dLevhDgWXbCaBfT
Face_Recognition/Sample generator.py
Line 8 in ba3a291
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Face recognition projects involve developing systems or algorithms capable of identifying or verifying individuals by analyzing and comparing patterns based on their facial features. These projects typically utilize computer vision, machine learning, and deep learning techniques to extract, analyze, and recognize facial features from images or video frames.
Here's an outline of steps involved in a typical face recognition project:
Data Collection: Gathering a dataset of facial images. This dataset should ideally cover various lighting conditions, facial expressions, angles, and ages to ensure the model's robustness.
Preprocessing: Cleaning and preparing the dataset for training. This may involve tasks such as resizing images, normalizing pixel values, and aligning faces to a standard position.
Feature Extraction: Extracting discriminative features from facial images. Common techniques include Principal Component Analysis (PCA), Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), or deep learning-based feature extraction using Convolutional Neural Networks (CNNs).
Model Training: Training a machine learning or deep learning model on the extracted features. Popular algorithms include Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), or deep neural networks like Convolutional Neural Networks (CNNs). The model learns to differentiate between different faces based on the extracted features.
Evaluation: Assessing the performance of the trained model using metrics like accuracy, precision, recall, or F1-score. Evaluation is typically done on a separate validation or test dataset to gauge the model's generalization capability.
Deployment: Integrating the trained model into a real-world application or system. This could involve deploying the model on a server, embedding it into a mobile app, or integrating it with existing security systems.
Fine-tuning and Maintenance: Continuously improving the model's performance by fine-tuning parameters, retraining with new data, or adapting to changing environmental conditions. Maintenance involves ensuring the system remains accurate and reliable over time.
Face recognition projects find applications in various domains such as security (access control, surveillance), authentication (biometric identification), human-computer interaction (smartphones, virtual reality), and marketing (audience analysis, targeted advertising). However, it's essential to address privacy concerns and ethical considerations when deploying face recognition systems to safeguard individuals' rights and prevent misuse.
pip install opencv-python
First Run Sample Generator File
After Complete Run Model Trainer File # It Creates trainer.yml File In Trainer Folder
Now Run Face_Recognition File