Enhancing the detection of unsafe Acts and Conditions using Face Recognition Technique in the Industry

This study proposes a dual faceted framework aimed at addressing this issue through advanced technological interventions. The first component focuses on the detection of unsafe acts by leveraging environmental sensors and behavioral monitoring systems. These systems utilize machine learning algorithms to analyze contextual data and flag actions or situations that deviate from predefined safety standards. The second component integrates face recognition technology for the identification of individuals associated with the detected unsafe acts. By delineating the detection of unsafe behaviors from the identification process, this approach maintains a clear functional separation, ensuring transparency and purpose specificity in the application of face recognition. The paper presents comprehensive methodologies, including the design and deployment of sensor networks, the development of behavior analysis algorithms, and the integration of biometric identification systems. Real world case studies demonstrate the effectiveness of the proposed approach in diverse settings such as construction sites, industrial plants, and public venues. Furthermore, this work critically examines ethical implications, with a particular emphasis on privacy concerns, potential algorithmic biases, and the need for regulatory oversight. Recommendations are provided to balance technological innovation with ethical responsibility, fostering the adoption of safety enhancing measures while safeguarding individual rights and freedoms.