Automatic identity recognition of ear images represents an active area of interest within the biometric community. The human ear is a perfect source of data for passive person identification. Ear images can be captured from a distance and in a covert manner; this makes ear recognition technology an attractive choice for security applications and surveillance in addition to related application domains. Differing from other biometric modalities, the human ear is neither affected by expressions like faces are nor do need closer touching like fingerprints do. In this paper, a deep learning object detector called faster region based convolutional neural networks (Faster R-CNN) is used for ear detection. A convolutional neural network (CNN) is used as feature extraction. principal component analysis (PCA) and genetic algorithm are used for feature reduction and selection respectively and a fully connected artificial neural network as a matcher. The testing proved the accuracy of 97.8% percentage of success with acceptable speed and it confirmed the accuracy and robustness of the proposed system.

Traditional networking solutions are unable to meet modern computing needs due to the expanding popularity of the internet, which requires increased agility and flexibility. To meet these objectives, software-defined networking (SDN) arises. A controller is a major element that will determine if SDN succeeds or fails. Various current SDN controllers in many sectors must be evaluated and compared. The performance of two well-known SDN controllers, POX and Ryu, is evaluated in this research. We used the Mininet-WiFi emulator to implement our work and the distributed internet traffic generator (D-ITG) to assess the aforementioned controllers using delay, jitter, packet loss, and throughput metrics. What is new in our research is the study of network performance in two different types of transmission media: wired and wireless. The speed of the wired medium was chosen to be fast ethernet, which was not previously studied. In addition, the size of the packet was varied among 128, 256, 512, and 1,024 bytes. The comparison was performed on three topologies (single, linear, and tree). The experimental results showed that Ryu offers significantly lower latency, jitter, and packet loss than POX in most scenarios. Also, the Ryu controller has higher throughput than POX, especially on wireless networks.

Earprint has interestingly been considered for recognition systems. It refers to the shape of ear, where each person has a unique shape of earprint. It is a strong biometric pattern and it can effectively be used for authentications. In this paper, an efficient deep learning (DL) model for earprint recognition is designed. This model is named the deep earprint learning (DEL). It is a deep network that carefully designed for segmented and normalized ear patterns. IIT Delhi ear database (IITDED) version 1.0 has been exploited in this study. The best obtaining accuracy of 94% is recorded for the proposed DEL.

<span lang="EN-US">Face recognition is a considerable problem in the field of image processing. It is used daily in various applications from personal cameras to forensic investigations. Most of the provides solutions proposed based on full-face images, are slow to compute and need more storage. In this paper, we propose an effective way to reduce the features and size of the database in the face recognition method and thus we get an increase in the speed of discrimination by using half of the face. Taking advantage of face symmetry, the first step is to divide the face image into two halves, then the left half is processed using the principal component analysis (PCA) algorithm, and the results are compared by using Euclidian distance to distinguish the person. The system was trained and tested on ORL database. It was found that the accuracy of the system reached up to 96%, and the database was minimized by 46% and the running time was decreased from 120 msec to 70 msec with a 41.6% reduction.</span>

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