In this paper an Iris image is encrypted based on QR (quick response) code and chaotic map. The main idea of the proposed system is generating a QR code depending on the input text and then extract the features from QR code by using convolution, these features are used for key generation. After that the permuted iris image is encrypted by using generated key, after that the resulting image will be encrypts using 2D logistic map. The randomness of generated key is tested using the measures of NIST, and quality of images that encrypted in this method are tested by using security analysis tests such as PSNR, UACI, NPCR, histogram, correlation and entropy. The security analysis shows that the proposed system is secure for iris image encryption.

This paper discusses the results of a study that aimed to develop an eigenface technique known as (PC) 2A that collect the image of the original face with its vertical and horizontal projections. The basic components of the image were analyzed in the image enrichment section. An evaluation of the proposed method demonstrates that it costs less than the standard eigenface technique. Moreover, the experimental results show that a front-end database that has a gray level for each person has one training image; thus, in terms of accuracy, it was possible to get a 3-5% result for the proposed (PC)2A, which is higher than the precision of the standard eigenface technique. The main objective of this paper is to demonstrate the weaknesses and strengthens of the facial recognition approach as an identifier known as eigenfaces. This aim was achieved by using the principal components analysis algorithm based on the images of previously stored training data. The outcomes show the strength of the proposed technique, in which it was possible to obtain accuracy results of up to 96%, which in turn provides support for developing the technique proposed in this paper in the future because this work is of great importance in the field of biological treatments, the need for which has significantly increased over the last 5 years.

Security is one of the main sources of information protection, especially sensitive information that is transmitted over the Internet. Encryption is one of the most important elements used, which is an effective and necessary element to provide high-level security communication between different entities by transmitting unclear and encrypted information that does not allow unauthorized person to access, the method of choosing the appropriate and correct encryption algorithm is important to provide a secure connection that provides a more efficient and accurate encryption system. In this paper, we will review the algorithms (Triple DES, AES & HiSea) for secret key encryption that are most commonly used for this type of encryption.

The security of home doors has become one of the necessities in this era. The Internet of Things (IoT) technology has also entered into building the smart home. Therefore, it has become necessary to develop a facial recognition system that can be implemented on IoT devices. This study presented a method to recognize faces using the efficientnet-b4. Transfer learning with fine-tuning was used here due to the small dataset size and high accuracy (accuracy of Top-1= 82.9% and accuracy of Top-5 = 96.4%) of EfficientNet-B4 and it has fewer parameters (19.5 M) than the previously known model and this is what we are looking for in order to implement it on the Raspberry Pi. After training and saving the model, it is converted into a lightweight model and transferred to the Raspberry to distinguish faces. The results showed that the model had an accuracy of 97%, despite the fact that the collected images were taken in different lighting, different places, and different facial expressions.

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