Advancing Person Detection and Identification through Machine Learning Innovations




Person Identification


IT Services & Consulting



In a Nutshell.

● Harnessing the power of machine learning, we have crafted an impeccable solution for person detection that seamlessly identifies individuals within videos sourced from a spectrum of contexts, including security, surveillance, and computer vision applications.

● The cornerstone of our approach lies in the adoption of YOLO version 8, enabling precise person detection in both videos and images.

● Our client’s experience was significantly enriched as we automated a previously time-consuming manual process, resulting in streamlined identification with a remarkable accuracy exceeding 95%.

Interested to know more?

Our client is a prominent staffing enhancement service provider in Japan, providing businesses with an all-inclusive resolution to fulfill their workforce demands. Backed by a highly proficient and seasoned workforce, they deliver cost-efficient, top-notch, and punctual solutions across various sectors.
Our client grapples with multiple obstacles concerning video analysis and human identification. Primarily, their manual approaches to person identification and frame selection prove ineffectual and laborious, motivating them to explore an automated remedy to enhance operational efficiency. Secondly, ensuring accuracy in person identification holds paramount significance, particularly in the realm of security and surveillance applications.

The client is in need of a more reliable system that guarantees meticulous recognition of specific individuals within video footage. Moreover, they are in pursuit of a versatile solution capable of streamlining the intricate and subjective frame selection process, with applicability spanning diverse domains such as security, childcare, and office environments.


Innovature has engineered a robust and advanced system for person detection and identification through a synergistic integration of cutting-edge technologies. The core of this system harnesses the prowess of YOLO version 8, ensuring impeccable person detection accuracy across both videos and images.

The innovation continues with frame grouping, employing metric learning techniques that enable the streamlined organization and identification of images exhibiting similarities. Selecting the most representative image from each group is optimized through the utilization of the OpenCV library, which considers visual quality as a determining factor.

To elevate facial features to the next level, the system leverages GFPGAN, a state-of-the-art deep learning technique. This augmentation culminates in remarkable facial detail enhancement.

For the ultimate precision in individual identification within the processed images, the dlib library is seamlessly integrated.

The outcome is an unparalleled system that presents a comprehensive and efficient solution for meticulous person identification. Its far-reaching impact spans safety, efficiency, and the elevation of personalized experiences across a myriad of applications.


1. Accurate Person Identification: Up to 98% accuracy in recognizing individuals, enhancing security and surveillance.
2. Time-Saving Frame Selection: Save up to 70% of the time by automatically extracting high-quality frames.
3. Easy Human Identification: Streamlined identification with up to 95% accuracy using advanced algorithms.
4. Time-Saving Automation: Save up to 90% of the time by automating frame selection.
5. Efficient Office Implementation: Employee attendance tracking, access control, and security features.

Tech Stack

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