**4. Challenges and future work**

Multimodal biometric systems are used to overcome the unimodal biometric system limitations by collecting multiple traits from multiple sensors. However, such a system will decrease the performance by increasing the processing duration and verification steps, and this causes users' troubles. So for developing reliable and user-friendly biometric system, we fuse soft and primary biometrics to improve the overall performance of the primary biometric system.

Soft biometrics inherit the nonintrusiveness and computational efficiency, which allow for fast, enrolment-free, and pose-invariant biometric analysis. However biometric system based on soft biometric trait only cannot provide accurate recognition because they change over time and lack distinctiveness, so there are still many challenges in this area. Parameter tuning as fusion rules and decision threshold otherwise error rate will increase and this can be improved using fuzzy logic.

Soft biometrics are very sensitive to illumination, expression variations, and pose variation, so we can use deep learning for preprocessing and feature extraction. New soft biometric traits can be also introduced as relative between the size of the head and body and facial distance measurement.
