Abstract
This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.
Highlights
Face recognition systems benefit from multi-modal feature (MMF) sets and their performance can outway that of individual modalities [1]
We extend the the interval valued fuzzy TOPSIS (IVFT) technique for fusing information from multi-modal features of a 3D face recognition system
The fuzzy interval-valued TOPSIS (IVFT) approach to multi-criteria decision making for fusing multi-modal features in a 3D face recognition system is proposed
Summary
Face recognition systems benefit from multi-modal feature (MMF) sets and their performance can outway that of individual modalities [1]. The Cumulative Match Curve (CMC) which is a set of performance plots typically used in biometric systems may exhibit similar responses of the modalaties under the same environmental conditions or the number of parameters to deal with are large making the feature selection process a difficult task In such cases, subjective judgements that do not have a 100% certainty or due to lack of data or incomplete information lead to decision making under uncertainty [2]. Popular MCDM techniques include ELECTRE (Elimination et Choice Translating Reality) [9], SAW (Simple Adaptive Weighting) [10], TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) [8], AHP (Analytical Hierarchy Process) [8], ANP (Analytic Network Process) and SMART (Simple Multi Attribute Rating Technique) [11] to name a few Some of these techniques have been benchmarked for a rigorous classification problem in [12]. In [15], a correlation between individual and group judgements are first established which are used to adaptively modify the weights of experts that contribute to the group’s decision, thereby increasing the confidence in the individual’s judgement
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