Cylindrical Model-Based Head Tracking and 3D Pose Recovery from Sequential Face Images

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.

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Model-Based Real-Time Head Tracking

EURASIP Journal on Advances in Signal Processing ◽

10.1155/s1110865702206034 ◽

2002 ◽

Vol 2002 (10) ◽

Cited By ~ 14

Author(s):

Jacob Ström

Keyword(s):

Real Time ◽

Head Tracking ◽

Model Based

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New Sparse Facial Feature Description Model Based on Salience Evaluation of Regions and Features

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001415560078 ◽

2015 ◽

Vol 29 (05) ◽

pp. 1556007 ◽

Cited By ~ 3

Author(s):

Yue Zhao ◽

Jianbo Su

Keyword(s):

Face Recognition ◽

Evaluation Method ◽

Facial Feature ◽

Experimental Results ◽

Sparse Learning ◽

Feature Description ◽

Model Based ◽

Face Images ◽

Proposed Model ◽

Structured Sparse Learning

Some regions (or blocks) and their affiliated features of face images are normally of more importance for face recognition. However, the variety of feature contributions, which exerts different saliency on recognition, is usually ignored. This paper proposes a new sparse facial feature description model based on salience evaluation of regions and features, which not only considers the contributions of different face regions, but also distinguishes that of different features in the same region. Specifically, the structured sparse learning scheme is employed as the salience evaluation method to encourage sparsity at both the group and individual levels for balancing regions and features. Therefore, the new facial feature description model is obtained by combining the salience evaluation method with region-based features. Experimental results show that the proposed model achieves better performance with much lower feature dimensionality.

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