Automatic Recognition Of Individuals Research Articles

Pig Face Recognition Model Based on a Cascaded Network

HighlightsA pig face recognition model that cascades the pig face detection network and pig face recognition network is proposed.The pig face detection network can automatically extract pig face images to reduce the influence of the background.The proposed cascaded model reaches accuracies of 99.38%, 98.96% and 97.66% on the three datasets.An application is developed to automatically recognize individual pigs.Abstract. The identification and tracking of livestock using artificial intelligence technology have been a research hotspot in recent years. Automatic individual recognition is the key to realizing intelligent feeding. Although RFID can achieve identification tasks, it is expensive and easily fails. In this article, a pig face recognition model that cascades a pig face detection network and a pig face recognition network is proposed. First, the pig face detection network is utilized to crop the pig face images from videos and eliminate the complex background of the pig shed. Second, batch normalization, dropout, skip connection, and residual modules are exploited to design a pig face recognition network for individual identification. Finally, the cascaded network model based on the pig face detection and recognition network is deployed on a GPU server, and an application is developed to automatically recognize individual pigs. Additionally, class activation maps generated by grad-CAM are used to analyze the performance of features of pig faces learned by the model. Under free and unconstrained conditions, 46 pigs are selected to make a positive pig face dataset, original multiangle pig face dataset and enhanced multiangle pig face dataset to verify the pig face recognition cascaded model. The proposed cascaded model reaches accuracies of 99.38%, 98.96%, and 97.66% on the three datasets, which are higher than those of other pig face recognition models. The results of this study improved the recognition performance of pig faces under multiangle and multi-environment conditions. Keywords: CNN, Deep learning, Pig face detection, Pig face recognition.

Towards Accurate Pupil Detection Based on Morphology and Hough Transform

Automatic recognition of individuals is very important in modern eras. Biometric techniques have emerged as an answer to the matter of automatic individual recognition. This paper tends to give a technique to detect pupil which is a mixture of easy morphological operations and Hough Transform (HT) is presented in this paper. The circular area of the eye and pupil is divided by the morphological filter as well as the Hough Transform (HT) where the local Iris area has been converted into a rectangular block for the purpose of calculating inconsistencies in the image. This method is implemented and tested on the Chinese Academy of Sciences (CASIA V4) iris image database 249 person and the IIT Delhi (IITD) iris database v1 using MATLAB 2017a. This method has high accuracy in the center and radius finding reaches 97% for 2268 iris on CASIA V4 image and 99.77% for 2240 iris images on IITD, the speed is acceptable compared to the real-time detection speed and stable performance.

Automatic biometric verification algorithm based on the bifurcation points and crossovers of the retinal vasculature branches

Biometric identification systems allow for the automatic recognition of individuals on one or more biometric characteristics. In this paper, we propose an automatic identity verification algorithm based on the structure of the vascular network of the human retina. More precisely, the biometric template consists of the geometric coordinates of bifurcation points and crossovers of the vascular network branches. The main goal of our work is to achieve an efficient system while minimising the processing time and the size of the data handled. Therefore, this algorithm uses a novel combination of powerful techniques for feature extraction based on mathematical morphology, like the watershed transformation for the segmentation of the retinal vasculature and hit-or-miss transform for the detection of bifurcation points and crossovers. We detail each step of the method from acquisition and enhancement of retinal images to signature comparison through automatic registration. We test our algorithm on a retinal images database (DRIVE). Finally, we present and discuss the evaluation results of our algorithm, and compare it with some of the literature.

Automatic acoustic identification of individuals in multiple species: improving identification across recording conditions.

Many animals emit vocal sounds which, independently from the sounds’ function, contain some individually distinctive signature. Thus the automatic recognition of individuals by sound is a potentially powerful tool for zoology and ecology research and practical monitoring. Here, we present a general automatic identification method that can work across multiple animal species with various levels of complexity in their communication systems. We further introduce new analysis techniques based on dataset manipulations that can evaluate the robustness and generality of a classifier. By using these techniques, we confirmed the presence of experimental confounds in situations resembling those from past studies. We introduce data manipulations that can reduce the impact of these confounds, compatible with any classifier. We suggest that assessment of confounds should become a standard part of future studies to ensure they do not report over-optimistic results. We provide annotated recordings used for analyses along with this study and we call for dataset sharing to be a common practice to enhance the development of methods and comparisons of results.

Biometric application and classification of individuals using postural parameters

This Biometrics refers to automatic recognition of individuals based on their physiological and/or behavioral characteristics. The displacement of the centre of pressure (COP) is a measure that has been successfully employed in studies regarding the postural control. So why not using the postural control as a behavioral characteristic to recognize individuals? The purpose of this study is to recognize individuals and to classify them by their age, gender, height and weight using postural features. The recognition approaches: Linear Discriminant Analysis (LDA), Support Vectors Machine (SVM) and K Nearest Neighbors (KNN) are applied to these biometric application. The postural features using in this application are extracted from data from twenty five healthy participants (average age 31 ± 11 years) standing in orthostatic position on an electromagnetic platform. The features are divided into three categories: classical features (extracted directly from the signals), mPCA features (extracted from the components trembling and rambling issued from the mPCA decomposition) and wavelet features (extracted from the components detail signal level 3 and detail signal level 2 issued from the wavelet decomposition. The results show that the ten subjects chosen for the recognition application are identified by the LDA with the rate of 80.43%. The classification applications (according to age, gender, height and weight) are performed for the all participants and the best resulted classification rates ranged from 82.61% to 93.48%. Most of these best rates are performed by the SVM classifiers using the totality of the features.

Processing the Segmentation Stage of an Iris Recognition System Through Evolutionary Algorithm

The automatic recognition of individuals through the iris characteristics is an e±cient biometric technique that is widely studied and applied around the world. Many image processing stages are necessary to make possible the representation and the interpretation of the iris information in an iris recognition system. The first processing stage should be the segmentation of the iris region in an eye image. A new iris segmentation method based on evolutionary algorithms is proposed and described in this work. The new method applies a special implementation of the called Memetic Algorithm which is used here to find the circles that represent the external iris border and the pupil border in an edge map. The e±ciency of the proposed method was compared to the e±ciency of a method which uses the Circular Hough Transform, a well known algorithm employed to ¯nd circles in an edged image. Images from a public database were used to evaluate the algorithms. Experimental results show that the innovator idea of using evolutionary algorithm for iris segmentation has an encouraging performance.