Research shows improved facial recognition accuracy and low-resolution performance

The rise of biometric identification and verification systems deployed in the edge infrastructure has created a gap in accurate and efficient facial recognition models. Traditional facial recognition technology based on deep convolutional neural networks (DCNN) has limitations. These include susceptibility to external factors like occlusions, variations in lighting conditions, and facial expressions, which can compromise the accuracy of identification. Therefore, engineers need a new method that can overcome these challenges.

Engineers need a face attribute recognition technology that uses optimized …

accuracy biometric biometric identification biometrics biometrics at the edge biometrics news biometrics research conditions convolutional neural networks edge external facial facial recognition facial recognition technology gap identification infrastructure lighting limitations low networks neural networks performance recognition research resolution systems technology the edge verification verification systems