Know How Machine Learning Changed Facial Recognition Technology

In today’s era of digital transformation, the convergence of biometrics and machine learning has been paving the way for technological breakthroughs. Facial recognition technology is certainly one aspect of it but has come a long way in being more robust, intuitive, and accurate. From secure authentication and user verification to biometric attendance systems and more, the real-time applications of AI-enabled facial recognition are far-fetched.

TimeCentral facial recognition system brings the power of deep neural networks or machine learning that is capable of capturing highly precise visual data for fast and secure identification and authentication. Through machine learning, training data is served by the algorithm to deliver highly accurate facial recognition results.

A computer system does not read a face in the image; it interprets the values underlying different pixels that make up the image. This allows the deep neural network to find different patterns. At the time of training, the weights assigned to these signals are wide-ranging and help deliver more accurate and better results. For effective identification, modern face recognition systems consider the facial features of the individuals and match these with pre-trained data for screening.

TimeCentral has an 8-inch dual camera powered by machine learning technology that helps capture 20,000 faces in only 1 second. The facial recognition system is a perfect convergence of modern technology and human intelligence that can deliver high accuracy levels up to 97.3%.

Cutting-edge face recognition systems leverage biometric features and transform this data collected from a 2D image into a set of numbers. These numbers or vectors define the face, which is then compared with the database to validate the same features. In many cases, 2D tracking cameras or 3D sensors are used to capture facial features more accurately from different angles. TimeCentral combines the power and intuitiveness of machine learning to ensure high accuracy rates for our facial recognition system.    

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