Name
Abstract | ||
|---|---|---|
An increased number of bio-inspired face recognition systems have emerged in recent decades owing to their intelligent problem-solving ability, flexibility, scalability, and adaptive nature. Hence, this survey aims to present a detailed overview of bio-inspired approaches pertaining to the advancement of face recognition. Based on a well-classified taxonomy, relevant bio-inspired techniques and their merits and demerits in countering potential problems vital to face recognition are analyzed. A synthesis of various approaches in terms of key governing principles and their associated performance analysis are systematically portrayed. Finally, some intuitive future directions are suggested on how bio-inspired approaches can contribute to the advancement of face biometrics in the years to come. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2791121 | ACM Computing Surveys |
Keywords | Field | DocType |
Algorithms,Security,Performance,Face recognition,bio-inspired computing,feature selection,optimization,evolutionary algorithms,artificial neural networks,swarm intelligence | Data mining,Facial recognition system,Evolutionary algorithm,Computer science,Bio-inspired computing,Biometrics,Scalability | Journal |
Volume | Issue | ISSN |
48 | 1 | 0360-0300 |
Citations | PageRank | References |
2 | 0.35 | 94 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bisan Alsalibi | 1 | 12 | 1.87 |
| Ibrahim Venkat | 2 | 70 | 14.37 |
| K. G. Subramanian | 3 | 339 | 59.27 |
| Syaheerah Lebai Lutfi | 4 | 23 | 3.92 |
| Philippe De Wilde | 5 | 192 | 23.86 |