Automated Human Identification and Obstacle Avoidance for Visually Impaired


  • Priyanka Kumari
  • . .





DWT, classifier, obstacle avoidance, ultrasonic sensor, PIR sensor.


This paper provides a method for human identification and obstacle avoidance for visually impaired. Visually impaired people faces lots of difficulty in accomplishing their day to day activities. Among one such difficulty is to recognize the person, this paper comes up with a technique which will helps blind people to identify person approaching them. Here DWT technique is used for face recognition. In this technique the entire image is decomposed into discrete wavelet bands. From this bands required features of image is obtained. This features when subjected to classifier gives proper output by identifying the person. Another part of paper deals with obstacle avoidance by using a blind stick. Blind stick uses an ultrasonic sensor and PIR sensor that detect obstacle at a distance of 100 cm. This stick can be used as alert signal for blind people.




[1] Diwakar Srinath A, Praveen Ram AR, Siva R, Kalaiselvi VKG & Ajitha G, “HOT GLASS Human face, object and textual recognition for visually challengedâ€, 2nd international conference on computing and communication technology, (2017), pp.111-116.

[2] Koteswara Rao M, Veera Swamy K & Anitha sheela K,“Face recognition using DWT and eigenvectorsâ€, 1st international conference on emerging technology trends in Electronics, communication & networking, (2012).

[3] Mukhedkar MM & Powalkar SB, “Fast Face Recognition Based on Wavelet Transform on PCAâ€, International conference on Energy system and Applications, (2015), pp.761-764.

[4] Chaurasia S & Kavitha KVN, “An Electronic Walking Stick for Blindsâ€, International conference on information communication and Embedded system, (2014), pp.1-5.

[5] Yaji GS, Sarkar S, Manikantan K & Ramachandran S, “DWT feature extraction based face recognition using intensity mapped unsharp masking and laplacian of gaussian filtering with scalar multiplierâ€, Procedia Technology, Vol.6, (2012), pp.475-484.

[6] Samaria FS & Harter AC, “Parameterization of a stochastic model for human face identificationâ€, Proceedings of IEEE workshop on Applications of Computer Vision, (1994), pp.138-142.

[7] Roure J & Faundez-Zanuy M, “Face recognition with small and large size databasesâ€, Proceedings 39th annual International carnahan conference on security technology, (2005), pp. 153-156

[8] Wafar E & Elleithy K, “Sensor-Based Assistive Devices for Visually-Impaired People: Current Status, Challenges, and Future Directionsâ€, Ed. Panicos kyriacou sensors, Vol.17, No.3, (2017).

[9] Liu J, Liu J, Xu L & Jin W, “Electronic travel aids for the blind based on sensory substitutionâ€, 5th International Conference on Computer Science and Education, (2010), pp.1328-1331.

[10] Sánchez J & Elias M, “Guidelines for designing mobility and orientation software for blind childrenâ€, Proceedings of the IFIP Conference on Human-Computer Interaction, (2007).

[11] Kanagaratnam K, “Smart Mobility Cane: Design of Obstacle Detectionâ€, EE 4BI6 Electrical Engineering Biomedical Capstones, (2009).

View Full Article:

How to Cite

Kumari, P., & ., . (2018). Automated Human Identification and Obstacle Avoidance for Visually Impaired. International Journal of Engineering & Technology, 7(3.6), 9–12.
Received 2018-07-02
Accepted 2018-07-02
Published 2018-07-04