Face-and-Emotion-Detection

1. Description

This project can be used for:

1. Detect faces from live images taken from webcam.
2. Detct faces in short live video sequence.
3. Detect faces in mask wearing images.
4. Detect and recognize emotions

2. Background:

Face detection is a computer vision problem that involves finding faces in photos.State-of-the-art face detection can be achieved using a Multi-task Cascade CNN via the MTCNN library. Locating a face in a photograph refers to finding the coordinate of the face in the image, whereas localization refers to demarcating the extent of the face, often via a bounding box around the face. Detected faces can then be provided as input to a subsequent system, such as a face recognition system. Face detection is a necessary first-step in face recognition systems, with the purpose of localizing and extracting the face region from the background. A number of deep learning methods have been developed and demonstrated for face detection.

One of the more popular approaches is called the “Multi-Task Cascaded Convolutional Neural Network,” or MTCNN .

Used DeepFace library to detect emotions of a person using webcam

During the COVID-19 pandemic, face detection systems have become crucial as they can effectively detect faces even when individuals are wearing masks, enabling reliable identification and surveillance in public settings.

3. Use cases:

Face detection has numerous practical applications across various industries. Some key use cases include:

1. Security and surveillance: Face detection systems are used in video surveillance to identify and track individuals of interest, enhancing security in public spaces, airports, and other high-security areas.
   

2. Biometric identification: Face detection is widely used in biometric systems for identity verification and access control, such as unlocking smartphones or granting access to secure facilities.
   

3. Emotion recognition: By analyzing facial expressions, face detection can be used to infer emotions, enabling applications in market research, customer sentiment analysis, and improving human-computer interaction.