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首页> 外文期刊>Arabian Journal for Science and Engineering >Bezier Cohort Fusion in Doubling States for Human Identity Recognition with Multifaceted Constrained Faces
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Bezier Cohort Fusion in Doubling States for Human Identity Recognition with Multifaceted Constrained Faces

机译：Bezier队列融合在倍增状态下，具有多方面约束面的人类身份识别

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摘要

Cohort selection benefits a biometric system by providing the information collected from non-match templates, whereas fusion benefits a system by combining information collected from different sources or from same source in different ways. The benefits of both approaches are availed here by proposing a cohort selection technique which is exploited prior to fusion and after fusion of matching scores for a face recognition system. Two robust facial features, viz. scale-invariant feature transform and speeded up robust features, are used here. This study presents a novel way of fusion based on cohort selection unlike the traditional levels of fusion (i.e., sensor, feature, match score, rank and decision level fusions). Cohort-based fusion is performed in two different fashionspre-cohort fusion and post-cohort fusion. In case of early fusion, fusion rules like sum, max, min and average rules are applied before cohort selection to be performed. In contrast, the cohort selection is followed by the fusion in post (or late)-cohort fusion. Union operation is applied as late fusion rule. The matching scores are normalized by T-norm cohort score normalization technique prior to be compared with the threshold value to govern the decision of acceptance by the system. The experiments are carried out on FEI and the Look-alike (IIIT Delhi) face databases. The outcomes of the proposed method are looked to be encouraging and much convincing over non-cohort systems and state-of-the-art methods.
机译：队列选择通过提供从非匹配模板收集的信息来利益生物识别系统，而融合通过将从不同来源或不同方式从不同来源的信息组合来通过组合来自不同来源的信息来帮助系统。这里通过提出融合在融合之前和融合面部识别系统的匹配分数之后，通过提出群组选择技术来利用两种方法的益处。两个强大的面部特征，viz。在此处使用尺度不变的功能变换和加速强大的功能。本研究介绍了一种基于群组选择的融合方式，与传统的融合水平不同（即传感器，特征，匹配得分，等级和决策级别融合）。基于群组的融合是以两种不同的方式进行的两种不同的时装队员 - 队列融合和队列后融合。在早期融合的情况下，在待执行的队列选择之前应用融合规则，如SUM，MAX，MIN和平均规则。相比之下，队列选择之后是帖子（或晚期）融合的融合。联盟操作作为晚期融合规则应用。在与阈值进行比较之前，匹配分数由T-Narm Cohort评分标准化技术进行标准化，以控制系统的接受决定。实验是在FEI和视野（IIIT DELHI）面部数据库上进行的。拟议的方法的结果被认为是鼓励和令人信服的非队员系统和最先进的方法。

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