Mean ensemble (ie averaging predictions from multiple models) is a commonly-used
technique in machine learning that improves the performance of each individual model. We …

Abstract Training Deep Convolutional Neural Networks (DCNN) with large-scale face
images takes a significant amount of processing resources and the tuning/optimization time …

Deep feature learning has become crucial in large-scale face recognition, and margin-
based loss functions have demonstrated impressive success in this field. These methods …

Multi-modal deep metric learning is crucial for effectively capturing diverse representations
in tasks such as face verification, fine-grained object recognition, and product search …

''Easy/hard sample” is a popular parlance in machine learning. Learning difficulty of samples
refers to how easy/hard a sample is during a learning procedure. An increasing need of …

This study compares the performance of ArcFace and Dlib models in face recognition with
YOLOv8 used for face detection on a limited dataset. The evaluation used metrics such as …

Face recognition aims to identify or verify the identity of a person through his or her face
images or videos. It is one of the most important research topics in computer vision with …

Abstract Machine learning (ML) is one of the fastest-developing topics today, straddling the
boundary between statistics and computer science, as well as data science. It is a type of …

This research aims to develop an Android-based attendance system using the DeepFace
library and Location-Based Service (LBS). The traditional attendance system often faces …