Abstract | ||
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This paper presents a new approach to face recognition, combining the techniques of computer vision and machine learning. A steady improvement in recognition performance is demonstrated. It is achieved by learning individual faces in terms of the local shapes of image boundaries. High-level facial features, such as nose, are not explicitly used in this scheme. Several machine learning methods are tested and compared. The overall objectives are formulated as follows: Classify the different tasks of "face recognition" and suggest an orderly terminology to distinguish between them. Design a set of easily and reliably obtainable descriptors and their automatic extraction from the images. Compare plausible machine learning methods; tailor them to this domain. Design experiments that would best reflect the needs of real world applications, and suggest a general methodology for further research. Perform the experiments and compare the performance. |
Year | DOI | Venue |
---|---|---|
1994 | 10.1080/08839519408945436 | APPLIED ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
face recognition,machine learning | Facial recognition system,Terminology,Computer science,Image processing,Feature (machine learning),Artificial intelligence,Quantization (signal processing),Machine learning | Journal |
Volume | Issue | ISSN |
8 | 1 | 0883-9514 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Libor Spacek | 1 | 98 | 15.73 |
Miroslav Kubat | 2 | 2384 | 231.57 |
Doris Flotzinger | 3 | 46 | 16.76 |