HomeElectronics NewsCapacitive Fingerprint Recognition Module

Capacitive Fingerprint Recognition Module

A capacitive fingerprint sensor detects patterns by discharging energy on touched capacitors and offers the flexibility to adapt to various finger conditions.

fingerprint sensor

Rajguru Electronics have launched R503 capacitive fingerprint recognition module featuring a new circular design with a two-color ring indicator LED control operating at DC 3.3V. According to the company’s claim, the modules can be seamlessly integrated into a wide range of final products, including access control systems, attendance trackers, safety deposit boxes, and more. The fingerprint recognition module claims to feature an integrated image collecting and algorithm chip in a single module. According to the press release issued by the company, the module offers flexibility to adapt to various finger conditions, including dry fingers, wet fingers, light texture fingerprints, and aged fingers, with a high recognition rate.

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According to the company’s claim, the fingerprint recognition module supports communication via a serial interface and can be connected to a microcontroller unit (MCU) operating at either 3.3V or 5V power. The data exchange format for the module is series when communicating with an upper computer. It applies to both universal asynchronous receiver-transmitter (UART) and Universal Serial Bus (USB) communication modes. For Personal computers (PC), a USB interface is highly recommended to enhance exchange speed, particularly in fingerprint scanning devices.

Some of the key features of fingerprint recognition include:

  • Interface: UART(TTL)
  • Resolution: 508 DPI
  • Voltage: DC 3.3V
  • Fingerprint Capacity: 200
  • Sensing array: 192*192 pixel
  • Working current: 20mA
  • Standby current: Typical touch standby voltage: 3.3V, Average current: 2uA
  • Fingerprint module external size: Diameter 28 (mm)
  • Fingerprint module inner size: Diameter 23.5 (mm)
Nidhi Agarwal
Nidhi Agarwal
Nidhi Agarwal is a Senior Technology Journalist at Electronics For You, specialising in embedded systems, development boards, and IoT cloud solutions. With a Master’s degree in Signal Processing, she combines strong technical knowledge with hands-on industry experience to deliver clear, insightful, and application-focused content. Nidhi began her career in engineering roles, working as a Product Engineer at Makerdemy, where she gained practical exposure to IoT systems, development platforms, and real-world implementation challenges. She has also worked as an IoT intern and robotics developer, building a solid foundation in hardware-software integration and emerging technologies. Before transitioning fully into technology journalism, she spent several years in academia as an Assistant Professor and Lecturer, teaching electronics and related subjects. This background reflects in her writing, which is structured, easy to understand, and highly educational for both students and professionals. At Electronics For You, Nidhi covers a wide range of topics including embedded development, cloud-connected devices, and next-generation electronics platforms. Her work focuses on simplifying complex technologies while maintaining technical accuracy, helping engineers, developers, and learners stay updated in a rapidly evolving ecosystem.

2 COMMENTS

  1. The image acquisition time is incredibly fast, clocking in at less than 0.2 seconds. The generation of feature points occurs swiftly, completing in under 500 milliseconds. During the power-on process, the initialization typically takes around 200 milliseconds.

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