Feature Sharing Applied to Palmprint Identification

2009 ◽  
Author(s):  
Ping Zheng ◽  
Nong Sang
Keyword(s):  
Palmprint Identification ◽  
Feature Sharing

scholarly journals Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks

2020 ◽  
Author(s):  
Ehsan Emad Marvasti ◽  
Arash Raftari ◽  
Amir Emad Marvasti ◽  
Yaser P. Fallah ◽  
Rui Guo ◽  
...  
Keyword(s):  
Object Detection ◽  
Deep Networks ◽  
Feature Sharing

Phase-Edge Features and the Syntax of Polarity Particles

2013 ◽  
Vol 44 (3) ◽  
pp. 345-389 ◽  
Author(s):  
J.-Marc Authier
Keyword(s):  
Intermediate Layer ◽  
Feature Sharing ◽  
Embedded Clauses ◽  
Edge Features

In this article, I argue that the phase edge in the C field shares features via Agree with an intermediate layer (FinP) and with a lower projection (ΣP), allowing it to determine the type of clause and its polarity. I adopt a feature-sharing relation of Agree that connects all of the polarity features present on heads (be they Σ, Fin, and, in some cases, VFoc) to a polarity feature in Force, the relevant phase-edge position for clausal typing. This explains, among other things, why embedded clauses containing a polarity feature can only satisfy the selectional properties of a particular class of (matrix) verbs.

Palmprint Identification using SVM and CNN Method

2021 ◽  
Author(s):  
Ayu Wirdiani ◽  
Darma Putra ◽  
Made Sudarma ◽  
Rukmi Sari Hartati
Keyword(s):  
Palmprint Identification

scholarly journals Palmprint Recognition in Uncontrolled and Unco-operative Environment

2021 ◽  
Vol 9 (8) ◽  
pp. 1711-1718
Author(s):  
Jayakrishnan S Kumar
Keyword(s):  
Deep Learning ◽  
Learning Rule ◽  
Industrial Applications ◽  
Palmprint Recognition ◽  
User Cooperation ◽  
Victim Identification ◽  
Palmprint Identification ◽  
On Line ◽  
End To End ◽  
Area Unit

Abstract: On-line palmprint recognition and latent palmprint identification unit two branches of palmprint studies. The previous uses middle-resolution footage collected by a camera in an exceedingly} very well-controlled or contact-based surroundings with user cooperation for industrial applications and so the latter uses high resolution latent palmprints collected in crime scenes for rhetorical investigation. However, these two branches do not cowl some palmprint footage that have the potential for rhetorical investigation. Attributable to the prevalence of smartphone and shopper camera, further proof is at intervals the variability of digital footage taken in uncontrolled and uncooperative surroundings. However, their palms area unit typically noticeable. To visualize palmprint identification on footage collected in uncontrolled and uncooperative surroundings, a novel palmprint info is established Associate in nursing AN end-to-end deep learning rule is projected. The new data named NTU Palmprints from the net (NTU-PI-v1) contains 7881 footage from 2035 palms collected from the net. The projected rule consists of Associate in Nursing alignment network and a feature extraction network and is end-to-end trainable. The projected rule is compared with the progressive on-line palmprint recognition ways that and evaluated on three public contactless palmprint infos, IITD, CASIA, and PolyU and a couple of new databases, NTU-PI-v1 and NTU contactless palmprint info. The experimental results showed that the projected rule outperforms the current palmprint recognition ways that. Keywords: Biometrics, criminal and victim identification, forensics, palmprint recognition

Cyclical change and problems of projection

2019 ◽  
pp. 13-32
Author(s):  
Elly van Gelderen
Keyword(s):  
The Other ◽  
The Third ◽  
Cyclical Change ◽  
Diachronic Change ◽  
Feature Sharing ◽  
Linguistic Cycle ◽  
Syntactic Derivation ◽  
Labelling Algorithm

In diachronic change, specifiers are reanalysed as heads and heads as higher heads. When the older specifiers and heads are renewed, a linguistic cycle emerges. Explanations provided for these cycles include structural and featural economy (e.g. van Gelderen 2004; 2011). Chomsky’s (2013, 2015) focus on labelling as unconnected to merge makes it possible to see the cycles in another way, namely as resolutions to labelling problems. The Labelling Algorithm (LA) operates after merge is complete, when a syntactic derivation is transferred to the interfaces. When a head and a phrase merge, the LA determines that the head is the label by Minimal Search. Where two phrases merge, the LA cannot find the head and one of the phrases has to either move or share features with the other. This chapter argues that, in addition to Chomsky’s resolutions to labelling paradoxes, reanalysing a phrase as a head also resolves the paradox. It also shows that the third factor principle minimal search is preferable over feature-sharing. The change from phrase to head is frequent, as eight cross-linguistically attested changes show. In addition, in the renewal stage of a cycle, adjuncts are frequently incorporated as arguments showing a preference of set-merge (feature-sharing) over pair-merge.

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