Biometric signature verification system based on freeman chain code and k-nearest neighbor

Sun

et al. 2020

Preprint

In stock farming, the body size parameters and weight of yaks can reasonably reﬂect the growth and development characteristics, production performance and genetic characteristics of yaks. However, it is diﬃcult for herders to measure the body size and weight of yaks by traditional manual methods. Fortunately, with the development of edge computing, herders can use mobile devices to estimate the yak’s body size and weight. The purpose of this paper is to provide a machine vision-based yak weight estimation method for the edge equipment and establish a yak estimation comprehensive display system based on the user’s use of the edge equipment in order to maximize the convenience of herdsmen’s work. In our method, a set of yak image foreground extraction and measurement point recognition algorithm suitable for edge equipment were developed to obtain yak’s measurement point recognition image, and the ratio between body sizes was transmitted to the cloud server. Then, the body size and weight of yaks were estimated using the data mining method, and the body size estimation data were constantly displayed in the yak estimation comprehensive display system. 25 yaks in different age groups were randomly selected from the herd to perform experiments. The experimental results show that the foreground extraction method can obtain segmentation image with good boundary, and the yak measurement point recognition algorithm has good accuracy and stability. The average error between the estimated values and the actual measured values of body height, oblique length, chest depth, cross height and body weight is 1.95%, 3.11%, 4.91%, 3.35% and 7.79%, respectively. Compared with the traditional manual measurement method, the use of mobile end to estimate the body size and weight of yaks can improve the measurement eﬃciency, facilitate the herdsmen to breed yaks, reduce the stimulation of manual measurement on yaks, and lay a solid foundation for the ﬁne breeding of yaks in Sanjiangyuan region.

Section: Measuring Point Recognition Algorithms 321 Recognition Algmentioning

confidence: 99%

Body Weight Estimation of Yak Based on Cloud Edge Computing

Sun

et al. 2020

Preprint

“…In Figure 7, heads that have been ignored are indicated in yellow. Afterwards, their profile curves of the left upper part to the center were encoded by 24 − neighborhood chain codes; and, curvature of each point was calculated [27]. In this case, a point with the maximum curvature represents the point of withers of the corresponding yak.…”

Section: Measuring Point Recognition Algorithms 321 Recognition Algmentioning

confidence: 99%

Machine Vision-enabled Yak Weight Estimation on Edge Devices

Sun

et al. 2020

Preprint

In the field of stock farming, body dimensions and weight of a yak may reasonably reveal its growing characters, productivity and genetic characteristics. However, it is arduous for the herdsman to manually measure the body and dimension of yaks. Fortunately, with the development of mobile computing and edge devices, it is preferable and possible for the herdsman to estimate the yaks weight and size with handheld devices (e.g., mobile phones). This paper aims at providing machine visual-based yak body height and weight estimate method for edge devices. In our method, to begin with a foreground image of the yak is extracted; and measuring point identification is carried out to identify measuring points of the yak. Then, a ratio of its body dimensions is acquired. Both body dimensions and weight of the yak are acquired through comparison with relevant data. 25 yaks in different age groups were randomly selected from a herd to perform experiments. As indicated by corresponding experimental results, the foreground extraction approach has the potential to generate split images with good boundaries. As for the measuring point identification method selected for the yak, it features preferable accuracy and stability. For example, estimated values of its standing height, body length, chest depth, hipcross height and body weight, average errors between the measured values and them are proven to be 1.95%, 3.11%, 4.91%,3.35% and 7.79% respectively. By contrast to the traditional measuring approaches, the proposed method may improve measurement efficiency and reduce stimulation caused by manual measurement to the yak.

“…Face recognition using a deep-learning framework hierarchically merges both global and local features, while handling nuisance factors. Deep learning architectures used in biometrics typically take a Neural Network (NN) approach, typically the Multi-Layer Perceptron (MLP) [96,97]. An MLP is constructed from layers of neurons with an input layer, one or many hidden layers, and an output layer.…”

Section: Deep Learning For Biometricsmentioning

confidence: 99%

Pattern Mining Approaches Used in Sensor-Based Biometric Recognition: A Review

et al. 2019

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being-acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems.