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Anti Infrared Facial Recognition Camera/CCTV Privacy Glasses

Anti-infrared Facial Recognition Camera/CCTV Privacy Glasses can resist the facial recognition by monitoring camera and is based on the understanding of application technology principle of current mainstream facial recognition monitoring camera and its application.

Infrared Facial Recognition Camera/CCTV Is The Current Mainstream Recognition Technology

Why is facial recognition the current mainstream recognition application technology? It mainly lies in its naturality and feature of not being perceived by the subject. The so-called naturality refers to “such recognition way is the same as the biological characteristic when human (or even other creatures) carries out the individual recognition”. For example, in the facial recognition, human distinguishes and confirms the identity through observing and comparing face. Voice recognition and figure recognition have naturality while fingerprint recognition and iris recognition do not have naturality, for human or other creatures do not distinguish the individual through such biological characteristic.
The feature of not being perceived is also very important for a recognition method of monitoring camera of facial recognition. This will not make the recognition method offensive. Besides, as the human attention is not easily be drawn by them, deception is not easily caused. Facial recognition technologies gain facial image information by visible light while fingerprint recognition or iris recognition have to depend on electronic pressure sensor or infrared ray to collect the information they need. These special collection ways are easily perceived by human, so disguise or deception is more likely to appear.

As the facial recognition technology has two features which are difficult to be replaced, it may have a broader application in the future, with the installation of more public monitoring cameras.

Anti-Infrared Facial Recognition Camera/CCTV Privacy Glasses Are Mainly Used For Anti-Dynamic Facial Recognition

First of all, the definition and classification of facial recognition should be understood:
Facial recognition: a biological recognition technology which identifies a subject base on the facial characteristic information it gathers through camera pictures and video. The technology can be divided into static facial recognition and dynamic facial recognition.

Static Facial Recognition

Static facial recognition may be called cooperative facial recognition. Generally, it is used in the specific short distance area or a small scope for recognition of the subject. The static recognition requires strict conditions of angle, distance and position. Most subjects actively cooperate in the recognition, for example, facial unlocking of Iphone, facial sign-in of attendance system of the company, human and certificate identity verification and other scenarios.

Dynamic Facial Recognition

Dynamic facial recognition may also be called the completely senseless facial recognition. Generally, it is used within the short and medium-long distance. As long as the subject appears within the scope, the subject is automatically recognized. In other words, when the subject goes naturally, the camera will take a snapshot and collect the facial information, for the purpose of dynamic facial recognition. Compared with the static facial recognition, dynamic facial recognition has “non-compulsory” and “no active contact” advantages. Primarily, it is in the targeted promotion and application of smart retail, security field and other scenarios through monitoring camera.
Anti-facial Recognition Privacy Glasses intervene from Infrared Camera/CCTV. The facial recognition system is composed of the following:
● Facial image collection and detection
● Facial image pre-processing
● Facial image characteristic extraction
● Matching and recognition
● Vivo detection
Facial Image Collection and Detection:
Facial Image Collection
Under suitable conditions, the facial image can be collected by the camera lenses. When the subject is within the shooting scope of collection device, the collection device will automatically search and take his facial image, such as static image, dynamic image, different positions and different expressions.

Facial Detection

Facial detection is mainly used for the pre-processing of facial recognition. In other words, to spot the position and size of a face in the image. Facial image contains rich mode characteristics, such as histogram, color, template, structure and Haar. Selected through algorithm, these characteristics are used to realize facial detection.

Facial Image Pre-processing

Facial image pre-processing refers to image processing based on the results of facial detection to serve the extraction of characteristics. Due to the restrictions and disturbances, the original image obtained by the system cannot be directly used in most cases. In the early stage of image processing, grey level correction, noise filtration and other image pre-processing must be done. As for the facial image, the pre-processing mainly includes light compensation, grey level transformation, histogram equalization, normalization, geometric correction, filtration and sharpening of facial image.

Facial Image Characteristic Extraction

The available characteristics of facial recognition system include visual characteristics, pixel statistical characteristics, transformation coefficient characteristics of facial image, and algebraic characteristic of facial image. The facial characteristic extraction is for some facial characteristics. The facial characteristic extraction is also called facial representation, which is a process of facial characteristic modeling.
To sum up, facial characteristic extraction methods can be divided into two categories: knowledge based representation method and algebraic characteristic or statistics based representation method. The knowledge-based representation method obtains favorable data for the facial classification through the shape description of facial organs and their distance. The characteristic component usually includes Euclidean distance between characteristics, curvature and angle. Face comprises eye, nose, mouth, chin and other parts. The geometric description of such parts and their structural relationship may be regarded as the important characteristics for facial recognition. These characteristics are called geometrical characteristics. The knowledge based facial representation mainly includes the geometrical characteristic based method and template matching method.