Palmprint is an important key player in biometric family and also informs some extra basic personality details of an individual. In this paper, we utilize these extra information and designed an automated mobile vision (MV) system to extract principal lines from human palm and analyze them for behavioral significances. Hence, the main concern of this paper is to come up with a simple yet powerful low-level MV solution to extract the complex challenging features from palmprint. In the proposed system, the computational tasks are offloaded to a dedicated palmistry server and efficiently minimizes the energy consumption of mobile device after performing some preliminary computational low-level tasks. The implementation is divided into four major phases: (i) hand-image acquisition and pre-processing, (ii) region-of-interest extraction from the palm images, (iii) post-processing to extract principal lines and (iv) features computation for behavior analysis. The basic palmistry uses line lengths, angles, curves and branches to identify a person’s behavior. The exhaustive experiments show that the proposed system achieves an average accuracy of 96%, 92% and 84% for heart, life and head line detection and personality prediction, respectively. Finally, mapping the extracted results with the original palmprint is augmented back to the use for better visualization.

中文翻译：

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个人性格行为分析掌上电脑

掌纹是生物识别家庭中的重要关键角色，并且还为个人提供了一些额外的基本性格细节。在本文中，我们利用这些额外的信息，设计了一个自动移动视觉（MV）系统，以从人的手掌中提取主要线条并对其行为意义进行分析。因此，本文的主要关注点是提出一个简单而强大的低级MV解决方案，以从掌纹中提取复杂的具有挑战性的特征。在提出的系统中，在执行一些初步的计算底层任务之后，将计算任务卸载到专用的手相服务器，并有效地最小化了移动设备的能耗。实施过程分为四个主要阶段：（i）手图像获取和预处理，（ii）从手掌图像中提取感兴趣区域，（iii）后处理以提取主线，以及（iv）为行为分析进行特征计算。基本手相学使用线长，角度，曲线和分支来识别人的行为。详尽的实验表明，该系统在心脏，生命和头线检测以及人格预测方面的平均准确率分别为96％，92％和84％。最后，将提取的结果与原始掌形图的映射关系又增加了使用范围，以实现更好的可视化效果。详尽的实验表明，该系统在心脏，生命和头线检测以及人格预测方面的平均准确率分别为96％，92％和84％。最后，将提取的结果与原始掌形图的映射关系又增加了使用范围，以实现更好的可视化效果。详尽的实验表明，该系统在心脏，生命和头线检测以及人格预测方面的平均准确率分别为96％，92％和84％。最后，将提取的结果与原始掌形图的映射关系又增加了使用范围，以实现更好的可视化效果。