Performance evaluation measures for face detection algorithms

2004 ◽  
Author(s):  
P. Sharma
Keyword(s):  
Performance Evaluation ◽  
Face Detection ◽  
Evaluation Measures ◽  
Detection Algorithms

Comparative Evaluation of Face Detection Algorithms

2020 ◽  
Author(s):  
Ahmed Yamout ◽  
Ahmed Abdelmawgood ◽  
Ebraam Sadick ◽  
Mohamed Naguib
Keyword(s):  
Face Detection ◽  
Comparative Evaluation ◽  
Detection Algorithms

scholarly journals Cost-Performance Evaluation of a Recognition Service of Livestock Activity Using Aerial Images

2021 ◽  
Vol 13 (12) ◽  
pp. 2318
Author(s):  
Darío G. Lema ◽  
Oscar D. Pedrayes ◽  
Rubén Usamentiaga ◽  
Daniel F. García ◽  
Ángela Alonso
Keyword(s):  
Performance Evaluation ◽  
Local Solution ◽  
Aerial Images ◽  
Cost Ratio ◽  
Activity Detection ◽  
Average Precision ◽  
Cost Performance ◽  
Detection Algorithms ◽  
Cloud Infrastructures ◽  
Animal Detection

The recognition of livestock activity is essential to be eligible for subsides, to automatically supervise critical activities and to locate stray animals. In recent decades, research has been carried out into animal detection, but this paper also analyzes the detection of other key elements that can be used to verify the presence of livestock activity in a given terrain: manure piles, feeders, silage balls, silage storage areas, and slurry pits. In recent years, the trend is to apply Convolutional Neuronal Networks (CNN) as they offer significantly better results than those obtained by traditional techniques. To implement a livestock activity detection service, the following object detection algorithms have been evaluated: YOLOv2, YOLOv4, YOLOv5, SSD, and Azure Custom Vision. Since YOLOv5 offers the best results, producing a mean average precision (mAP) of 0.94, this detector is selected for the creation of a livestock activity recognition service. In order to deploy the service in the best infrastructure, the performance/cost ratio of various Azure cloud infrastructures are analyzed and compared with a local solution. The result is an efficient and accurate service that can help to identify the presence of livestock activity in a specified terrain.

scholarly journals A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection

2018 ◽  
Vol 10 (8) ◽  
pp. 80
Author(s):  
Lei Zhang ◽  
Xiaoli Zhi
Keyword(s):  
Face Detection ◽  
Graphics Processing Units ◽  
High Performance ◽  
Feature Fusion ◽  
Local Context ◽  
Data Set ◽  
Global Context ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Benchmark Datasets

Convolutional neural networks (CNN for short) have made great progress in face detection. They mostly take computation intensive networks as the backbone in order to obtain high precision, and they cannot get a good detection speed without the support of high-performance GPUs (Graphics Processing Units). This limits CNN-based face detection algorithms in real applications, especially in some speed dependent ones. To alleviate this problem, we propose a lightweight face detector in this paper, which takes a fast residual network as backbone. Our method can run fast even on cheap and ordinary GPUs. To guarantee its detection precision, multi-scale features and multi-context are fully exploited in efficient ways. Specifically, feature fusion is used to obtain semantic strongly multi-scale features firstly. Then multi-context including both local and global context is added to these multi-scale features without extra computational burden. The local context is added through a depthwise separable convolution based approach, and the global context by a simple global average pooling way. Experimental results show that our method can run at about 110 fps on VGA (Video Graphics Array)-resolution images, while still maintaining competitive precision on WIDER FACE and FDDB (Face Detection Data Set and Benchmark) datasets as compared with its state-of-the-art counterparts.

scholarly journals An Innovative Face Detection Based on YCgCr Color Space

2018 ◽  
Author(s):  
Solly Aryza
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Image ◽  
Color Space ◽  
Gaussian Model ◽  
Detection Rates ◽  
Detection Algorithms ◽  
Lighting Conditions ◽  
Wide Range ◽  
Human Faces

It is very challenging to recognize a face from an image due to the wide variety of face and the uncertain of face position. The research on detecting human faces in color image and in video sequence has been attracted with more and more people. In this paper, we propose a novel face detection method that achieves better detection rates. The new face detection algorithms based on skin color model in YCgCr chrominance space. Firstly, we build a skin Gaussian model in Cg-Cr color space. Secondly, a calculation of correlation coefficient is performed between the given template and the candidates. Experimental results demonstrate that our system has achieved high detection rates and low false positives over a wide range of facial variations in color, position and varying lighting conditions.

scholarly journals Performance Evaluation in Machine Learning: The Good, the Bad, the Ugly, and the Way Forward

2019 ◽  
Vol 33 ◽  
pp. 9808-9814 ◽  
Author(s):  
Peter Flach
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Causal Inference ◽  
Latent Variable ◽  
Measurement Theory ◽  
Latent Variable Models ◽  
Data Set ◽  
Evaluation Measures ◽  
Classifier Evaluation ◽  
The Way

This paper gives an overview of some ways in which our understanding of performance evaluation measures for machine-learned classifiers has improved over the last twenty years. I also highlight a range of areas where this understanding is still lacking, leading to ill-advised practices in classifier evaluation. This suggests that in order to make further progress we need to develop a proper measurement theory of machine learning. I then demonstrate by example what such a measurement theory might look like and what kinds of new results it would entail. Finally, I argue that key properties such as classification ability and data set difficulty are unlikely to be directly observable, suggesting the need for latent-variable models and causal inference.

Sign in / Sign up

Export Citation Format

Share Document