**Author details**

Tiwari S, Singh A, and Singh SK et al. [39] propose an optimal framework for newborn recognition by fusing match scores from face and soft biometrics. Results on IMS-BHU Indian hospital dataset show that soft biometrics improve recognition rate by 5.6% over the primary biometric. However framework evaluated on one dataset has high-resolution image taken

Jaha, Emad, and Mark et al. [43] show clothing traits can be used for identification of individual where clothing descriptions might be the only available feature. An, Chen, Kafai, Yang, and Bhanu et al. [49] aim to improve the re-identification performance by re-ranking the returned results based on soft biometric attributes. Experiments on challenging benchmark

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.

In a holistic survey on soft biometrics for user identification, we have seen that there is no one best biometric technology since it depends on the application requirement. A zero false acceptance rate is needed, for example, in security, and the false rejection rate needs to decrease, but in the civilian application, we need the opposite, so for any biometric system, we need to find a good balance between authentication reliability and complexity. As a result, traditional biometrics suffer from low recognition rate because they need cooperation with the user, operate in the controlled environment, and introduce privacy concern. So using multi-biometrics is the solution, but still, the system suffers from computation cost and long processing steps. However, another possible solution is to use soft biometrics to increase the population cover-

VIPeR dataset show that reranking improves the recognition accuracy.

under controlled pose and illumination.

52 Machine Learning and Biometrics

**4. Challenges and future work**

**5. Conclusion**

age and decrease the system cost and complexity.

Abdelgader Abdelwhab1 and Serestina Viriri2 \*

\*Address all correspondence to: viriris@ukzn.ac.za

1 College of Computer Science and Information Technology, Sudan University of Science and Technology, Khartoum, Sudan

2 School of Maths, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa
