In the development process of modern artificial intelligence, datasets are the beginning of model training and the key point to improve the performance of algorithm. Whether it is computer vision data for autonomous driving or audio data for emotion analysis, high-quality datasets will provide more accurate capability for prediction. By leveraging these datasets, developers can better optimize the performance of AI systems to cope with complex real-life demands.

The multi-modal biometric system market is growing rapidly.

Due to ever-changing needs, the single-modal biometric identification cannot support complex and diversified identity verification scenarios. Many companies are trying to expand a single biometric system into multi-modal biometric products and solutions to meet diversified needs.

Compared with traditional biometric technology, multi-modal biometric technology is more complex and diverse. It can realize the combination of fingerprint recognition, face recognition, iris recognition, voiceprint recognition and other biometric technologies. The multi-modal biometric system can overcome some of the limitations of the single-modal biometric system. Integrating multiple biometric modes in a single scan can relieve the pressure of a single-modal system. Meanwhile, a combination of multiple biological identification methods can effectively reduce identification errors and better ensure safety.

In the post-epidemic era, the combined application of multiple biometric technologies for multi-modal biometrics is more flexible, allowing us to choose appropriate combination methods based on different application requirements and scene changes. This will surely become the development trend of the new generation of biometric technology.

Wide range of application scenarios for multi-modal biometric technology

In the financial industry, multi-modal biometric technology has been implemented earlier, mainly because it has excellent performance in security, wide-application, experience-friendliness and data management, which can effectively reduce financial fraud and protect user’s information and data security.

In the field of video analysis, multi-modal biometric technology can quickly find out a segment of an actor in a video or program, even without the adjustment of playing speed.

Multi-modal biometric data solution

In order to support the innovation of multi-modal biometric technology, Nexdata has developed the datasets “156 Hours — Lip Sync Multimodal Video Data”. Nexdata strictly abides by relevant regulations, and the data is collected with proper data collection authorization agreement.

156 Hours — Lip Sync Multimodal Video Data

Recording Environment: Using quiet sunny room to stimulate daytime outdoor driving scenes, signal to noise ratio 25~20dB

Recording Content: Short signals and spoken sentences

Recording Angle: Recording videos of front face, single side face, looking up, looking down, side face looking down and side face looking up all 6 different angles, and proximal and distant audio at the same time

Accuracy : Accuracy of sentence should not below 95%

In addition, Nexdata also supports personalized data collection and annotation services in the field of multi-modal recognition, such as 3D face, liveness detection, gestures and gait etc.

Multi-modal biometric technology will play an important role in the future application of biometric technology. However, the development of multi-modal biometrics technology is facing many challenges, and enterprises in the industry need to work together to promote the commercialization and large-scale application of multi-modal biometrics technology.

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High-quality datasets are the cornerstone of the development of artificial intelligence technology. Whether it is current application or future development, the importance of datasets is unneglectable. With the in-depth application of AI in all walks of life, we have reason to believe by constant improving datasets, future intelligent system will become more efficient, smart and secure.