Muzzle Point Research Articles

AI-Driven livestock identification and insurance management system

Cattle identification is pivotal for many reasons. Animal health management, traceability, bread classification, and verification of insurance claims are largely depended on the accurate identification of the animals. Conventionally, animals have been identified by various means such as ear tags, tattoos, rumen implants, and hot brands. Being non-scientific approaches, these controls can be easily circumvented. The emerging technologies of biometric identification are extensively applied for Human recognition via thumb impression, face features, or eye retina patterns. The application of biometric recognition technology has now moved towards animals. Cattle identification with the help of muzzle patterns has shown tremendous results. For precise identification, nature has awarded a unique Muzzle pattern that can be utilized as a primary biometric feature. Muzzle pattern image scanning for biometric identification has now been extensively applied for identification. Animal recognition via Muzzle pattern image for different applications has been proliferating gradually. One of those applications includes the identification of fake insurance claims under livestock insurance. Fraudulent animal owners tend to lodge fake claims against livestock insurance with proxy animals. In this paper, we proposed the solution to avoid and/or discard fraudulent claims of livestock insurance by intelligently identifying the proxy animals. Data collection of animal muzzle patterns remained challenging. Key aspects of the proposed system include: (1) the Animal face will be detected through visual using YOLO v7 object detector. (2) After face detection, the same procedures will apply to detect muzzle point (3) the muzzle pattern is extracted and then stored in the database. The System has a mean average precision of 100% for the face and 99.43% for the nose/muzzle point of the animal. Once the animal is registered in the database, the identification process is initiated by extracting unique nose pattern features with ORB and/or SIFT. Then it is matched using the pattern matchers like BFMatcher and/or FLANNMatcher for animal identification. The proposed model is more efficient and accurate as compared to concurrent approaches. The results extracted from this research study show 100% accurate identification.

Deep Transfer Learning-Based Animal Face Identification Model Empowered with Vision-Based Hybrid Approach

The importance of accurate livestock identification for the success of modern livestock industries cannot be overstated as it is essential for a variety of purposes, including the traceability of animals for food safety, disease control, the prevention of false livestock insurance claims, and breeding programs. Biometric identification technologies, such as thumbprint recognition, facial feature recognition, and retina pattern recognition, have been traditionally used for human identification but are now being explored for animal identification as well. Muzzle patterns, which are unique to each animal, have shown promising results as a primary biometric feature for identification in recent studies. Muzzle pattern image scanning is a widely used method in biometric identification, but there is a need to improve the efficiency of real-time image capture and identification. This study presents a novel identification approach using a state-of-the-art object detector, Yolo (v7), to automate the identification process. The proposed system consists of three stages: detection of the animal’s face and muzzle, extraction of muzzle pattern features using the SIFT algorithm and identification of the animal using the FLANN algorithm if the extracted features match those previously registered in the system. The Yolo (v7) object detector has mean average precision of 99.5% and 99.7% for face and muzzle point detection, respectively. The proposed system demonstrates the capability to accurately recognize animals using the FLANN algorithm and has the potential to be used for a range of applications, including animal security and health concerns, as well as livestock insurance. In conclusion, this study presents a promising approach for the real-time identification of livestock animals using muzzle patterns via a combination of automated detection and feature extraction algorithms.

Secure and Sustainable Framework for Cattle Recognition Using Wireless Multimedia Networks and Machine Learning Techniques

Towards the successful operation of any farm, effective livestock management is necessary. The efficiency, affordability, and scalability of management solutions play a crucial role in modern farming. In this paper, a novel low-cost framework is proposed to recognize for health monitoring of individual cattle based on their sensed behavioral activity data and muzzle (nose) pattern image database using incremental decision tree classification techniques and accelerometer-based activity monitoring method. The proposed system performs image prepossessing on the captured muzzle point images of cattle to mitigate the noise, enhance the contrast, and increase quality. We extract the minutiae features to improve the system’s accuracy for recognizing the cattle for accurate and early classification of behavioral activity for health monitoring. The proposed system uses a support vector machine and incremental decision tree classifiers to classify the extracted feature of cattle’s muzzle images and health database. The server has encrypted databases that consist of captured muzzle images, owner database, health sensor database provided by the owners and systems. We use a similarity score measurement using incremental decision tree classification for matching and classify the muzzle point featutes with the database for cattle identification and health monitoring. We also developed a prototype for evaluating the accuracy of the proposed system with 97.99% accuarcy for unique identification of individual cattle.

Shi-Tomasi corner detector for cattle identification from muzzle print image pattern

Since the last decade, researchers have been using image processing and machine learning to automatically analyse and identify patterns from images without any human intervention. Machine learning tools entail image classification and an efficient feature detection algorithm in the field of computer vision systems. There are various state-of-the-art feature detectors and descriptors available for an image recognition task in image processing and computer vision. In this article, the Shi-Tomasi corner detector has been explored for cattle identification. Two feature descriptors namely, Scale Invariant Feature Transform (SIFT) and Speed up Robust Features (SURF) are considered for extracting the features from the digital images. A linear dimensionality reduction algorithm to make computation faster, reduces the size of features in all cases. For classification, three classification techniques namely, Multi-layer Perceptron (MLP), Random Forest, and Decision tree are considered. For the experimental work, a dataset consists of 930 cattle muzzle point image patterns of distinct 4 breeds. A combination of Shi-Tomasi, SURF, and SIFT are implemented to achieve a recognition accuracy of 69.32%, 74.88%, and 79.60% with MLP classifier, Decision Tree classifier, and Random Forest classifier, respectively. The performance of the proposed system is evaluated based on the classifier-wise recognition accuracy, false-positive rate, true positive rate, and area under curve. Finally, the accuracy is improved by applying an ensemble method (adaptive boosting). The proposed system has reported an improved recognition accuracy of 83.35% by applying Bootstrapping Aggregation methodology along with Random Forest classifier and a combination of Shi-Tomasi, SURF, and SIFT features descriptor.

Cattle Recognition: A New Frontier in Visual Animal Biometrics Research

Visual animal biometrics is an emerging research field of computer vision, pattern recognition, and cognitive science. Recently, cattle recognition has played a significant role in understanding, controlling and the outbreak of critical diseases, vaccination, production management, traceability, and ownership assignment of a livestock animal. The traditional animal recognition methodologies, such as ear-tagging, freeze-branding, ear-tattoos, embedded microchips, ear tips or notches-based, and electrical-based marking approaches, have been applied to recognize individual livestock animal. However, standard animal recognition procedures are invasive. The performance of conventional methods is not good due to their vulnerability to losses, easy duplication, and fraud of embedded tag number. These are major security issues and challenges for the identification of cattle throughout the world. Visual animal biometric systems are gaining more proliferations due to widespread applications to recognize individual cattle based on their primary biometric muzzle point image characteristics. This paper aims to provide a comprehensive review of cattle recognition and tracking from non-biometric recognition approaches (classical animal recognition methods) to visual animal biometric systems using muzzle point image pattern along with measurements and interpretations based on current state-of-the-art methods. Moreover, this paper demonstrates the basic deployment of the animal biometric system to uniquely identify the animals using their biometric characteristics. This study can hopefully encourage new multidisciplinary researchers and scientists to provide excellent efforts for the designing and development of adequate algorithms for solving the classification and recognition problems. The literature review is followed by the presentation of references for more details, incorporating applications and current trends.

Group Sparse Representation Approach for Recognition of Cattle on Muzzle Point Images

The usage of computer vision adds a new paradigm in the field of animal biometric, and has recently received more attention due to the growing importance of identification and tracking of animal species or individual animals. Biometric characteristics help to develop a better representation and a better identification of different animal species and individual animals. In this work, we propose an effective approach for automatic cattle recognition based on the multiple features of muzzle points and the cattle face images. The proposed method deals the cattle recognition problem as a classification problem among the multiple linear regression models and provides a new theory for the recognition of individual cattle. The group sparse signal representation based classification offers the key to addressing this problem using L2-minimization. In this paper, a comparative study among the well-established handcrafted texture feature extraction techniques and the appearance-based feature extraction techniques is also presented. A detailed set of experimental results on muzzle point image database is also carried to prove the theory. Our method has achieved 93.87% identification accuracy which demonstrates the superiority of the proposed method than the other existing machine learning based recognition algorithms.

Deep learning framework for recognition of cattle using muzzle point image pattern

Animal biometrics is a frontier area of computer vision, pattern recognition and cognitive science to plays the vital role for the registration, unique identification, and verification of livestock (cattle). The existing handcrafted texture feature extraction and appearance based feature representation techniques are unable to perform the animal recognition in the unconstrained environment. Recently deep learning approaches have achieved more attention for recognition of species or individual animal using visual features. In this research, we propose the deep learning based approach for identification of individual cattle based on their primary muzzle point (nose pattern) image pattern characteristics to addressing the problem of missed or swapped animals and false insurance claims. The major contributions of the work as follows: (1) preparation of muzzle point image database, which are not publically available, (2) extraction of the salient set of texture features and representation of muzzle point image of cattle using the deep learning based convolution neural network, deep belief neural network proposed approaches. The stacked denoising auto-encoder technique is applied to encode the extracted feature of muzzle point images and (3) experimental results and analysis of proposed approach. Extensive experimental results illustrate that the proposed deep learning approach outperforms state-of-the-art methods for recognition of cattle on muzzle point image database. The efficacy of the proposed deep learning approach is computed under different identification settings. With multiple test galleries, rank-1 identification accuracy of 98.99% is achieved.

Muzzle point pattern based techniques for individual cattle identification

Animal biometrics-based recognition systems are gradually gaining more proliferation due to their diversity of application and uses. The recognition system is applied for representation, recognition of generic visual features and classification of different species based on their phenotype appearances, the morphological image pattern, and biometric characteristics. The muzzle point image pattern is a primary animal biometric characteristic for the recognition of individual cattle. It is similar to the identification of minutiae points in human fingerprints. This chapter presents an automatic recognition algorithm of muzzle point image pattern of cattle for the identification of individual cattle, verification of false insurance claims, registration, and traceability process. The proposed recognition algorithm uses the texture feature descriptors, such as speeded up robust feature and local binary pattern for the extraction of features from the muzzle point images at different smoothed levels of Gaussian pyramid. The feature descriptors acquired at each Gaussian smoothed level are combined using fusion weighted sum rule method. With a muzzle point image pattern database of 500 cattle, the proposed algorithm yields the desired level of identification accuracy. Moreover, the comparative analysis of experimental results for proposed work and appearance-based face recognition algorithms has been done at each level. The proposed work, therefore, can be a potential approach for the recognition of individual cattle using muzzle point image pattern.

Automatic identification of cattle using muzzle point pattern: a hybrid feature extraction and classification paradigm

Animal biometrics is an emerging research field that develops quantified methodologies for representing and detecting the visual appearances of animal based on generic features and primary biometric characteristics. The identification of individual cattle is an important issue for classification of different breads, their registration, traceability, health management, and verification of false insurance claim throughout the world. To solve these major problems, the muzzle (nose) point image pattern of cattle is a suitable and primary biometric characteristic for the recognition of cattle. The recognition of muzzle point images is similar to the recognition of minutiae points in the human fingerprints. In this paper, we propose a hybrid feature extraction approach for the automatic recognition and classification of cattle breeds based on captured muzzle point image pattern features using low- cost camera. The major contributions of this research is based on following aspects: (1) preparation of muzzle point image dataset, (2) extraction of salient set of features and (3) K-nearest neighbour (K-NN), Fuzzy-KNN, Decision Tree (DT), Gaussian Mixture Model (GMM), Probabilistic Neural Network (PNN), Multilayer Perceptron (MLP) and Naive Bayes classification models are applied to recognition, and classification of individual cattle using their muzzle point pattern. This paper, therefore, demonstrates the automatic recognition and classification of livestock using the set of extracted muzzle point image features. The experimental results show that proposed hybrid feature extraction and recognition approach outperforms the current state-of- the art method for the identification of individual cattle using their muzzle point image pattern.

Real-time recognition of cattle using animal biometrics

With the advent of efficient recognition techniques, animal biometric systems have gained more proliferation for the identification and monitoring of cattle. A cattle biometric system is a pattern recognition-based system for the identification of livestock. In this paper, we propose a novel muzzle point recognition based on Fisher locality preserving projection algorithm for the recognition of cattle in real time. We have captured images of animals using a surveillance camera and transferred them to the server by wireless network technology. The major contributions are as follows: (1) preparation of muzzle point database, (2) extraction of the salient set of features using proposed muzzle point recognition approach, and (3) evaluation and comparison analysis of the introduced method and several existing recognition algorithms on a standard benchmark protocol. The efficacy of proposed muzzle point recognition approach for cattle evaluates under identification settings and yields \(96.87\,\%\) recognition accuracy for identifying individual cattle. The proposed approach also valued the 10.25 sec recognition time for enrollment and identified individual cattle on different sizes of muzzle point images.