Face-voice based multimodal biometric authentication system via FaceNet and GMM.

Information security has become an inseparable aspect of the field of information technology as a result of advancements in the industry. Authentication is crucial when it comes to dealing with security. A user must be identified using biometrics based on certain physiological and behavioral markers. To validate or establish the identification of an individual requesting their services, a variety of systems require trustworthy personal recognition schemes. The goal of such systems is to ensure that the offered services are only accessible by authorized users and not by others. This case study provides enhanced accuracy for multimodal biometric authentication based on voice and face hence, reducing the equal error rate. The proposed scheme utilizes the Gaussian mixture model for voice recognition, FaceNet model for face recognition and score level fusion to determine the identity of the user. The results reveal that the proposed scheme has the lowest equal error rate in comparison to the previous work.

Modeling of the Acute Lymphoblastic Leukemia Detection by Convolutional Neural Networks (CNNs).

BACKGROUND: The techniques differed in many of the literature on the detection of Acute Lymphocytic Leukemia from the blood smear pictures, as the cases of infection in the world and the Kingdom of Saudi Arabia were increasing and the causes of this disease were not known, especially for children, which is a serious and fatal disease. OBJECTIVE: Through this work we seek to contribute to discover the blood cells affected by Acute Lymphocytic Leukem and to find an effective and fast method and to have the correct diagnosis as the time factor is important in the diagnosis and the initiation of treatment. which is based on one of the deep learning techniques that specialize in very deep networks, the use of one of the CNNs is VGG16. METHODS: Detection scheme is implemented by pre-processing, feature extraction, model building, fine tuning method, classification are executed. By using VGG16 pre-trained, and using SVM and MLP classification algorithms in Machine Learning. RESULTS: Our results are evaluated based on criteria, such as Accuracy, Precision, Recall, and F1-Score. The accuracy results for SVM classifier MLP of 77% accuracy at 0.001 learning rate and the accuracy for SVM classifier 75% at 0.005 learning rate. Whereas, the best accuracy result for VGG16 model was 92.27% at 0.003 learning rate. The best validation accuracy result was 85.62% at 0.001 learning rate.