Visible Light Facial Recognition Facts & Figures

 

Visible Light Facial Recognition has become the most spoken word in the current Biometric Industry. The recent advancements in deep learning led to phenomenal developments in Computer Vision Technology, especially in the area of facial recognition. With respect to facial recognition performance based on deep network, there are two significant factors: Structure of neural networks and quality of the data. Whereas, in real-time applications, illumination change, which is nothing but changes in the light intensity, is the most significant factor impacting the performance of facial recognition algorithms.

Biometrics and Facial Recognition

Biometrics is an essential scientific filed of biological pattern recognition. In that, facial recognition is one of the most attractive applications of biometric technology. Nonetheless, facial recognition in real applications still pose greater challenges due to the fact that face often undergo changes owing to various facial expression, different appearances and angles as well as environmental factors such as different light intensity.

However, with recent advancements in biometric technology together with deep learning and robust facial recognition algorithms, today it is made possible to verify individuals’ face regardless of the attire, facial expressions, accessories and environmental factors including light intensity and shadow.

Types of Facial Recognition

There are various facial recognition methods available, let’s take a closer look into the technical details for a deeper understanding.

Traditional Method

This method identifies unique facial features such as relative position, size and shape of individual’s facial attributes from an image and compares with other existing data to recognize a person. On the other hand, certain algorithms normalize a cluster of images taken from an individual and compress it into a reference image data which is then used for facial recognition. Since, this method extracts data from photographic image rather than the original face, it is susceptible to print and video attacks. 

Template Based Method

This method takes local facial features and their geometric relationship into consideration and constructs a facial template using certain statistical tools such as Support Vector Machines, Linear Discriminant Analysis, Kernel methods, Principal Component Analysis, etc.

Piecemeal Method of Analysis

This is considered to be one of the famous methods of facial analysis using the whole face as a model with most relevant facial characteristics such as eyes or a combination various facial attribute. This category also contains Hidden Markov Model and Feature Processing. 

3D Facial Recognition

This type of facial recognition employs 3D sensors to gather accurate details of facial attributes such as shape, size, position and contour of face, eyes, nose, chin, etc. from actual individuals by projecting structured light spectrum onto the face in three-dimensional angles. In addition, this method is not affected by light intensity and offers wide pose angle tolerance enhancing the precision of facial recognition. However, this technique is slightly sensitive to facial expressions as the data is already stored in pre-exiting three-dimensional data points. 

Skin Texture Analysis

This method extracts the visible details of the facial skin such as lines, patterns, spots, skin texture and pores of individuals into unique mathematical representation, this mathematical representation is then used for facial identification. Furthermore, this technique can also distinguish between identical pairs enhancing the precision of facial recognition.

Neural Networks & Deep Learning

Neural Networks-Based facial recognition is one of the most adopted methods in biometric technology. It also the most sophisticated procedure of multi-dimensional vector-based data analysis, which has taken the mathematical representation of facial attributes to a higher magnitude. In this method neural networks are used to recognise and align normalised faces by combining various statistical tools to form a hybrid methodology of analysis for a multi-layer perception. This multilayer perceptron is then used as a facial recognition system in association with deep learning technology to automatically adapt to the changes in individuals’ facial attributes or external environment.

Multiplex Facial Recognition

As every method has pros and cons, multiplex facial recognition utilizes all possible combinations of robust facial recognition methods such as Template-Based, 3D Recognition, Skin Textual Analysis, Neural Networks & Deep Learning, etc to form a powerful facial recognition algorithm offering outstanding performance with respect to speed and precision.

The facial recognition systems employing this kind of approach will show multiple attributes of various algorithms such as,

High Speed Recognition

Wide Pose Angle Tolerance

Precise Recognition at Flexible Distance

Less Sensitive to Facial Expressions

User-Identification with Facial Accessories

Powerful Anti-Spoofing Mechanisms

And so on.

ZKTeco is one of the few companies in the word, which offers biometric systems with combination of multiple facial recognition algorithms such as ProFace X Series, SpeedFace-V5L Series, etc featuring Smart Visible Light Facial Recognition technology with advanced biometric algorithms for both face and palm verification.

The ZK Visible Light Facial Recognition technology develops a 3D facial template model via deep learning technology and provides wide pose angle tolerance in all yaw, pitch and roll axis, which eliminates the need for maintaining a particular pose or angle offering a free range of motion and automatically captures faces of users. 

Still having confusions about Visible Light Facial Recognition technology? please don’t hesitate to contact our Technical Support Executives for further information.