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Face recognition is also known as facial detection. It’s an AI-based computer technology that helps to find and recognize human faces in digital videos or images. Face recognition for surveillance is, in most cases, used for tracking people in real-time. Various industries currently use this system, including:
- Social media
- Entertainment
- Law enforcement
- Biometrics
- Security
Face recognition uses machine learning and artificial neural network technologies. With the help of these technologies, facial detection plays a crucial role in face analysis, tracking and recognition.
- In face analysis, facial expressions are used to identify parts of a video or image, which should be the main points to focus on to determine emotions, gender, and age.
- In a facial detection system, face recognition data is needed to generate a faceprint and compare it against other faceprints in the database.
How Face Recognition Works
The latest SentiVeillance ALPR algorithm is based on a specific neural network known as a convolutional neural network. To perfectly match the faceprints, convolutional neural network processes every photo via a number of steps, including:
- Facial detection: The first crucial step is to detect a face within a larger scene or image. This process usually involves distinguishing face features from the surrounding environment and isolating their locations within the frame.
- Facial analysis: After detecting a face, the system analyses the key features. The analysis process is basically based on the face’s geometry. It measures different key points in the facial image. These key points are known as nodal points or landmarks and can include the length between the eyes, the contours of the nose, lips and cheekbones, and the shape of the jawline.
- Facial feature extraction: This process involves the analysis results in facial feature extraction. These facial features are used to create a face template or faceprint. In simple terms, it creates a digital map of the facial geometry.
- Matching: The resulting facial template or faceprint is matched against a huge database of known and stored face images. A sophisticated matching algorithm helps complete this task. The algorithm can handle variations in angles, facial expressions, and lighting.
The convolutional neural network changes each face pattern into a unique numerical code, with every faceprint presented as a numerical vector. Vectors that are closer to each other are likely to provide the same outcome, which is a face match.
These days, face detection has a broad spectrum of applications globally, from personal security and individual onboarding to the identification of people in gatherings and crowds. Once you decide to embrace this technology, work closely with an experienced provider.
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