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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：倍数状态下的Bezier队列融合，用于多面约束面孔的人类身份识别。

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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：同类群组选择通过提供从非匹配模板收集的信息而使生物测定系统受益，而融合则通过以不同方式组合从不同来源或从相同来源收集的信息而使系统受益。通过提出一种队列选择技术，可以在这两种方法中受益，该队列选择技术是在融合匹配分数之前和融合之后用于面部识别系统的。两个强大的面部特征，即。此处使用比例不变特征变换和加速健壮特征。这项研究提出了一种基于队列选择的新颖融合方式，与传统融合方式（即传感器，特征，匹配得分，等级和决策等级融合）不同。基于群组的融合以两种不同的方式进行：群组前融合和群组后融合。在早期融合的情况下，将在执行队列选择之前应用诸如sum，max，min和average规则之类的融合规则。相比之下，在队列（或后期）-队列融合中进行融合后，进行队列选择。应用联合运算作为后期融合规则。匹配分数通过T​​-norm队列分数归一化技术进行归一化，然后与阈值进行比较以控制系统接受的决策。实验是在FEI和相像（IIIT Delhi）人脸数据库上进行的。与非同类系统和最新方法相比，拟议方法的结果令人鼓舞，并且令人信服。

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