AbstractNowadays, there has been an increase in security concerns regarding fingerprint biometrics. This problem arises due to technological advancements in bypassing and hacking methodologies. This has sparked the need for a more secure platform for identification. In this paper, we have used a deep Convolutional Neural Network as a pre-verification filter to filter out bad or malicious fingerprints. As deep learning allows the system to be more accurate at detecting and reducing false identification by training itself again and again with test samples, the proposed method improves the security and accuracy by multiple folds. The implementation of a novel secure fingerprint verification platform that takes the optical image of a fingerprint as input is explained in this paper. The given input is pre-verified using Google’s pre-trained inception model for deep learning applications, and then passed through a minutia-based algorithm for user authentication. Then, the results are compared with existing models.

Emotions play a vital role in the efficient and natural human computer interaction. Recognizing human emotions from their speech is truly a challenging task when accuracy, robustness and latency are considered. With the recent advancements in deep learning now it is possible to get better accuracy, robustness and low latency for solving complex functions. In our experiment we have developed two deep learning models for emotion recognition from speech. We compare the performance of a feed forward Deep Neural Network (DNN) with the recently developed Recurrent Neural Network (RNN) which is known as Gated Recurrent Unit (GRU) for speech emotion recognition. GRUs are currently not explored for classifying emotions from speech. The DNN model gives an accuracy of 89.96% and the GRU model gives an accuracy of 95.82%. Our experiments show that GRU model performs very well on emotion classification compared to the DNN model.

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