Haar Cascade is a cost-effective and user-friendly machine learning-based
algorithm for detecting objects in images and videos. Unlike Deep Learning
algorithms, which typically require significant resources and expensive
computing costs, it uses simple image processing techniques like edge detection
and Haar features that are easy to comprehend and implement. By combining Haar
Cascade with OpenCV2 on an embedded computer like the NVIDIA Jetson Nano, this
system can accurately detect and match faces in a database for attendance
tracking. This system aims to achieve several specific objectives that set it
apart from existing solutions. It leverages Haar Cascade, enriched with
carefully selected Haar features, such as Haar-like wavelets, and employs
advanced edge detection techniques. These techniques enable precise face
detection and matching in both images and videos, contributing to high accuracy
and robust performance. By doing so, it minimizes manual intervention and
reduces errors, thereby strengthening accountability. Additionally, the
integration of OpenCV2 and the NVIDIA Jetson Nano optimizes processing
efficiency, making it suitable for resource-constrained environments. This
system caters to a diverse range of educational institutions, including
schools, colleges, vocational training centers, and various workplace settings
such as small businesses, offices, and factories. ... The system's
affordability and efficiency democratize attendance management technology,
making it accessible to a broader audience. Consequently, it has the potential
to transform attendance tracking and management practices, ultimately leading
to heightened productivity and accountability. In conclusion, this system
represents a groundbreaking approach to attendance tracking and management...

基于 Haar 级联与 OpenCV2 的人脸检测系统可用于出勤追踪，通过使用简单的图像处理技术与边缘检测，结合精选的 Haar 特征和先进的边缘检测技术，实现高准确性和稳健性，节省资源并提高效率，适用于教育机构、企事业单位等各类场景，可民主化出勤管理技术，提升生产力和责任感。

人力资源中的考勤管理自动化：使用计算机视觉与人脸识别的设计科学方法

Automating Attendance Management in Human Resources: A Design Science  Approach Using Computer Vision and Facial Recognition

Facial Expression Recognition is a vital research topic in most fields
ranging from artificial intelligence and gaming to Human-Computer Interaction
(HCI) and Psychology. This paper proposes a hybrid model for Facial Expression
recognition, which comprises a Deep Convolutional Neural Network (DCNN) and
Haar Cascade deep learning architectures. The objective is to classify
real-time and digital facial images into one of the seven facial emotion
categories considered. The DCNN employed in this research has more
convolutional layers, ReLU Activation functions, and multiple kernels to
enhance filtering depth and facial feature extraction. In addition, a haar
cascade model was also mutually used to detect facial features in real-time
images and video frames. Grayscale images from the Kaggle repository (FER-2013)
and then exploited Graphics Processing Unit (GPU) computation to expedite the
training and validation process. Pre-processing and data augmentation
techniques are applied to improve training efficiency and classification
performance. The experimental results show a significantly improved
classification performance compared to state-of-the-art (SoTA) experiments and
research. Also, compared to other conventional models, this paper validates
that the proposed architecture is superior in classification performance with
an improvement of up to 6%, totaling up to 70% accuracy, and with less
execution time of 2098.8s.

本研究提出了一种结合深度卷积神经网络（DCNN）和 Haar Cascade 深度学习架构的混合模型，用于将实时和数字面部图像分类到七种面部表情类别之一。实验结果表明，与其他模型相比，该架构在分类性能上具有显著优势，并且准确率高达 70％，执行时间短，为 2098.8 秒。