FACIAL RECOGNITION SYSTEM SENSORS AND ACTUATORS
- 2. Introduction • Facial recognition is a technology capable of matching a human face from a digital image or a video frame against a database of faces to confirm an individual's identity. • It is a biometric tehnology which identifies or verifies a person by analyzing their facial features.
- 3. How Facial recognition works 1. Image Aquisition: Capturing an image of the face using a camera. 2. Face Detection: Locating and isolating the face within the image. 3. Feature Extraction: Identifying key facial(e.g, eyes,nose,mouth). 4. Face Recognition: Comparing extracted features against a database of known faces. 5. Decision Making: Determining whether there is a match and powering an output.
- 4. Working principle Step 1: Image Input A photograph or video feed is captured. Step 2: Preprocessing Enhancing the image(e.g, adjusting brigthness,contrast). Step 3: Detection and Alignment Detecting the face using algorithms. Step 4: Feature Analysis Analyzing facial landmarks Step 5: Comparison Comparing extracted features with a database.
- 5. Tehnologies behind facial recognition 1. Machine Learning Algorithms: Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). 2. Learning: Convolutional Neural Networks (CNNs) for image processing. 3. Computer Vision: Techniques to interpret and process visual data. 4. Databases: Large datasets of facial images for training models.
- 6. Challenges in facial Recognition 1. Variability: Changes in lighting, angle, and expressions. 2. Occlusion: Faces partially covered can hinder detection. 3. Privacy Concerns: Ethical considerations regarding surveillance.
- 7. Common Techniques 1. Haar Cascade Classifier: An object detection method to identify faces. 2. Facial Landmark Detection: Identifying specific points on a face. 3. Face Embedding: Converting facial features into a numerical vector.
- 8. Applications 1. Security Systems: Used in airports, banks, and public spaces. 2. Access Control: Unlocking devices or doors through facial verification. 3. Social Media: Automatic tagging of individuals in photos. 4. Law Enforcement: Identifying suspects in investigations.
- 9. Future Trends 1. Increased Accuracy: Advancements in AI for better performance. 2. Integration with Other Technologies: Combining with IoT devices. 3. Ethical AI: Development of guidelines for responsible use.
- 10. Conclusion • Facial recognition is a powerful technology with diverse applications, presenting challenges and ethical considerations.
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