Transfer Learning Model for Human Facial Recognition for Cancelable Biometric

To recognize the emotion for cancellable biometric is a technique for predicting the emotional state of an individual on the basis of user responses including verbal or facial expressions. But in a variety of situations, facial expressions are a useful tool for identifying people's moods. This procedure aids in the identification of the posture and movement of the facial muscles. Various schemes have been proposed in the past years for facial recognition. The existing schemes are unable to achieve good accuracy and in this paper transfer learning-based method is proposed for cancellable biometrics' identification of face emotions. There are several phases involved in face emotion detection for cancellable biometrics, including pre-processing data, feature extraction, and facial emotion classification. The proposed method is implemented in python and compare with existing schemes in terms of accuracy, precision and recall