Iris Segmentation in the Wild Using Encoder-Decoder-Based Deep Learning Techniques

2021 ◽  
pp. 283-311
Author(s):  
Shreshth Saini ◽  
Divij Gupta ◽  
Ranjeet Ranjan Jha ◽  
Gaurav Jaswal ◽  
Aditya Nigam
Keyword(s):  
Deep Learning ◽  
Iris Segmentation ◽  
Learning Techniques ◽  
In The Wild

scholarly journals A Review of Facial Landmark Extraction in 2D Images and Videos Using Deep Learning

2019 ◽  
Vol 3 (1) ◽  
pp. 14 ◽  
Author(s):  
Matteo Bodini
Keyword(s):  
Deep Learning ◽  
Future Research ◽  
3D Face Reconstruction ◽  
Research Directions ◽  
Facial Expression Analysis ◽  
Facial Landmark ◽  
Learning Techniques ◽  
In The Wild ◽  
Future Research Directions ◽  
2D Images

The task of facial landmark extraction is fundamental in several applications which involve facial analysis, such as facial expression analysis, identity and face recognition, facial animation, and 3D face reconstruction. Taking into account the most recent advances resulting from deep-learning techniques, the performance of methods for facial landmark extraction have been substantially improved, even on in-the-wild datasets. Thus, this article presents an updated survey on facial landmark extraction on 2D images and video, focusing on methods that make use of deep-learning techniques. An analysis of many approaches comparing the performances is provided. In summary, an analysis of common datasets, challenges, and future research directions are provided.

scholarly journals AgriPest: A Large-Scale Domain-Specific Benchmark Dataset for Practical Agricultural Pest Detection in the Wild

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1601
Author(s):  
Rujing Wang ◽  
Liu Liu ◽  
Chengjun Xie ◽  
Po Yang ◽  
Rui Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Benchmark Dataset ◽  
Domain Specific ◽  
Pest Monitoring ◽  
Field Environment ◽  
Learning Techniques ◽  
Monitoring Applications ◽  
In The Wild ◽  
Bounding Boxes

The recent explosion of large volume of standard dataset of annotated images has offered promising opportunities for deep learning techniques in effective and efficient object detection applications. However, due to a huge difference of quality between these standardized dataset and practical raw data, it is still a critical problem on how to maximize utilization of deep learning techniques in practical agriculture applications. Here, we introduce a domain-specific benchmark dataset, called AgriPest, in tiny wild pest recognition and detection, providing the researchers and communities with a standard large-scale dataset of practically wild pest images and annotations, as well as evaluation procedures. During the past seven years, AgriPest captures 49.7K images of four crops containing 14 species of pests by our designed image collection equipment in the field environment. All of the images are manually annotated by agricultural experts with up to 264.7K bounding boxes of locating pests. This paper also offers a detailed analysis of AgriPest where the validation set is split into four types of scenes that are common in practical pest monitoring applications. We explore and evaluate the performance of state-of-the-art deep learning techniques over AgriPest. We believe that the scale, accuracy, and diversity of AgriPest can offer great opportunities to researchers in computer vision as well as pest monitoring applications.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

Deep Learning Techniques for Koala Activity Detection

2018 ◽  
Author(s):  
Ivan Himawan ◽  
Michael Towsey ◽  
Bradley Law ◽  
Paul Roe
Keyword(s):  
Deep Learning ◽  
Activity Detection ◽  
Learning Techniques
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