Vol. 3 No. 1 (2021)

Open Access

Article

Article ID: 655

Research on the Problems and Countermeasures of Salary Management in Small and Medium-sized Enterprises

by Ke Meng, Guiyao Lu, Mingyang Shen

Human Resources Management and Services, Vol.3, No.1, 2021; 415 Views, 127 PDF Downloads

Small and medium-sized enterprises as the main body of Chinese enterprises should be an important driving force for China's economic development. However, the problems of salary management faced by small and medium-sized enterprises are increasingly hindering their own survival and development. Whether it is standing on the position of the enterprise or the workers, the pay problem is that they are more concerned about the problem, while the remuneration is also an indispensable modern means of competition and incentives. Salary management is not only indispensable content of enterprise human resources, but also the establishment of modern enterprise system, and optimization of the allocation of social resources requirements. Enterprise salary management operation flexibility or not, directly affect the production and operation management, which will affect the long-term development of enterprises. This paper analyzes the problems of salary management in small and medium-sized enterprises (SMEs), such as unreasonable pay system, lack of forward-looking management system, and so on, and analyzes and discusses their own countermeasures.

Open Access

Article

Article ID: 656

Automatic Detection of Face in Video Sequences by using Extended Semi Local Binary Patterns

by Ithaya Rani Panneerselvam, Hari Prasath T.

Human Resources Management and Services, Vol.3, No.1, 2021; 384 Views, 29 PDF Downloads

Machine analysis of detection of the face is an active research topic in Human-Computer Interaction today. Most of the existing studies show that discovering the portion and scale of the face region is difficult due to significant illumination variation, noise and appearance variation in unconstrained scenarios. To overcome these problems, we present a method based on Extended Semi-Local Binary Patterns. For each frame, an aggregation of the pixel values over a neighborhood is considered and a local binary pattern is obtained. From these a binary code is obtained for each pixel and then histogram features is computed. Adaboost algorithm is used to learn and classify these discriminative features with the help of exemplar face and non-face signature of the images for detecting the location of face region in the frame. This Extended Semi Local Binary Pattern is sturdy to variations in illumination and noisy images. The developed methods are deployed on the real time YouTube video face databases and found to exhibit significant performance improvement owing to the novel features when compared to the existing techniques.