Biometric Time Clocks Gaining Popularity Amongst HR Professionals

With the latest recession having huge impact on businesses, many employers have been forced to cut costs with any means possible.  Usually the first thing to happen is the reduction in staff.  However, what many employers fail to realize that instead of reducing staff, they can maximize the use of their existing HR budgets by keeping better records of their hourly employees.  One of the latest trends sweeping the globe that can help curb unnecessary costs is by using a biometric time clock.

While electronic time clocks have been popular as well in recent years, they definitely have their shortcomings.  For example, employees who use PINS or swipe access cards to punch in/out are easily able to manipulate the system by having their friends enter their PIN or swipe their badge for them.  This is often referred to as buddy punching and costs companies worldwide millions of dollars every year.

The next step in the evolution of the time clock is to eliminate this possibility which is where biometric time clocks come in. These units act very similar to traditional time cards or time punching machines.  Instead of an employee punching a time card to begin or end their shift, they simply swipe their hand under a biometric scanner or fingerprint across a fingerprint reader.

Biometric Security on Your Laptop

If you use a password to secure your laptop, your password might be able to be solved by someone else. However, if you use the biometric system, it would be very difficult to solve.

This system is capable of detecting fingerprints or retina, so that only the owner can open the lock. By using biometric systems, other people will not be able to open the existing system on your laptop without having your fingerprints or retina.

That is why, some computer manufacturers are now offering laptops that are built with biometric fingerprint identification systems. This system is useful to prove the authenticity of the user based on the similarities of fingerprints that are stored when the software is first run.

Is the Gunvault GVB1000 Mini Vault a Good Gun Safe?

So, you're looking for a small but secure biometric (fingerprint recognition) gun safe. Gunvault is one of the premier manufacturers of biometric safes, and the Gunvault GVB1000 Mini Vault is no exception.

Gunvault GVB1000 Mini Vault Overview

• Dimensions: 8.1 x 4.9 x 12"
• Weight: 8 lbs
• Lock: Biometric fingerprint recognition
• Body: 16-gauge steel
• Interior: Coated in soft foam
• Requires 1 9-volt battery or included AC adapter
• Interior lighting
• Stores up to 30 different fingerprint profiles
• Generally fits one handgun and a clip or two

The overall opinion of the Gunvault GVB1000 across the internet and among enthusiasts is very positive. It works very well as a simple but effective personal handgun safe, especially for storing your firearm close to you at night or just for keeping your valuables safe. The safe is mountable to any flat surface, although we'd obviously recommend installing it on something heavy or well built as the safe itself is not very heavy.

So, How Secure is the Gunvault GVB1000 Mini Vault?

Like the other gun safes by Gunvault, this is a biometric gun safe. It uses high tech fingerprint recognition technology for access, and is very accurate. It can store up to 30 different sets of fingerprints (although we're not sure why you'd need to give that many people access) and it will not at all open for anyone whose fingerprints don't match. Gunvault's biometric technology will continually update and refine the fingerprint profiles over time to make sure it is as accurate as possible.

The great thing about biometric safes is that they're easy to access in the dark. Since there is no fumbling around with physical keys or combinations, and all you have to do is press your finger to the pad for 3 seconds, you can have quick access in the case of, say, someone breaking into your house at night. This is one of the things we really like about the GVB1000. It also has a nice, low-intensity interior light, which is handy in a scenario like this, too. If you can convince the wife to let you drill into the night stand to mount this gun safe, you'll have a perfect place to store your hand gun at night.

Unlike many other gun safes, the Gunvault GVB1000 requires a 9-volt battery. This could be a bit of a hassle, as not many electronics use 9-volt batteries these days. It does come with a backup AC adapter, though, and a single 9-volt battery is rated to last about a year in the unit, so it's not a huge problem.

What's the Final Say On The GunVault GVB1000 Mini Vault?

Like most of Gunvault's other products, this is a quality safe if you're using it for its intended purpose. It's inexpensively priced for the technology and Gunvault has great customer service if you have any issue with the product, but you shouldn't have any problems, anyway. In conclusion, we would definitely recommend this gun safe.

What did we like about the Gunvault GVB1000 Mini Vault Gun Safe?

• Biometric technology is convenient and secure
• Works well as a bedside gun safe if bolted down
• Great value for the money

What didn't we like as much?

• Requires a 9-volt battery
• Might need to record your fingerprint a few times before it's 100% accurate

This gun safe is for:

• People looking for a bedside gun safe
• Those looking for something inexpensive but still secure
• People with children who want to keep their gun out of their hands

All in all, this is a great buy for the money.

If you're on the market for a gun safe, I would strongly suggest reading my site on gun safe reviews before deciding on a safe. There are many different models on the market, and not all are created equal. Get the unbiased scoop from me, a 20-year gun enthusiast. A biometric gun safe is a great addition to your home if you make the right purchase.

Article Source: http://EzineArticles.com/?expert=Frank_Gusso

 Gunvault GVB1000 Mini Vault

Barska Biometric Safe - Fingerprint Access To Your Valuables

If you own a handgun and there are children in your household, the Barska Biometric Safe could be the ideal solution. This safe only allows registered people access to the contents. This is achieved by the use of the Biometric Pad that can be programmed to recognise your fingerprints and will allow entry in about 3 seconds. The advantage of this system is because you do not require a key, there is no need to worry about finding it in an emergency, no need to fiddle with the lock, trying to fit the key. The Biometric Safe can even be opened in the dark. Imagine trying to remember a combination number if you are panicking - no need.

Barska Biometric Safe 

• It is a solidly built unit, weighing 31lbs.
• The internal measurements are 16.25W x 7H x14.25D
• Has mounting holes to fit to Floor or Wall complete with Mounting Kit
• Requires 4 x AA batteries
• Stores up to 30 fingerprint readings
• Also includes 2 Access Keys should the Batteries go flat
• Comes with 1Yr limited Warranty

Considering the size of the safe, it will accommodate a surprising amount of firearms and valuables. Reviews from owners state that it will house two guns with ammo and several spare magazines along with other valuables and documents,

One of the features is a beep when the safe is opened. This appears to be quite controversial with quite a few reviewers. Many said they would like to disable it, and some have gone to the trouble of disconnecting the buzzer. Just be aware this is in breach of the warranty terms. The positive reason for having the buzzer, is that it will sound with any attempt to open the safe, and if for any reason the safe door is left open for more than a minute, the alarm will sound continuously until it is closed.

The Honest Truth on Biometrics in Schools

By now many school principals, superintendents and administrators have probably heard of school lunch biometrics, or the use of devices such as fingerprint readers to recognize students and allow for the automated payment and accounting of school lunch purchases. Some may be wondering how to sort the promise from the hype, the information from the misinformation.

While school lunch biometrics can legitimately address a host of problems from slow lunch lines, lost lunch money, cumbersome payment, lunch fraud and bullying, to falling National School Lunch Program (NSLP) participation, the devil is in the details. Of course, it all comes down to the bottom line: labor, cost efficiency, and return on investment (ROI). Here I'll honestly discuss the pluses and minuses of school lunch biometrics versus more traditional technologies so administrators can decide if it makes sense for their schools.

How do school lunch biometric systems work and do they protect privacy?

In most school lunch biometric systems, students place a forefinger on a small fingerprint reader by the register. In seconds, the system translates the electronic print into a mathematical pattern, discards the fingerprint image, and matches the pattern to the student’s meal account information. Food Service Solutions (FSS) biometric software, for example, plots 27 points on a grid that correspond with the fingerprint's ridges to achieve positive identification, but saves no actual fingerprint image.

Facial Recognition Gone Wrong

"John H. Gass hadn't had a traffic ticket in years, so the Natick resident was surprised this spring when he received a letter from the Massachusetts Registry of Motor Vehicles informing him to cease driving because his license had been revoked. It turned out Gass was flagged because he looks like another driver, not because his image was being used to create a fake identity. His driving privileges were returned but, he alleges in a lawsuit, only after 10 days of bureaucratic wrangling to prove he is who he says he is. And apparently, he has company. Last year, the facial recognition system picked out more than 1,000 cases that resulted in State Police investigations, officials say. And some of those people are guilty of nothing more than looking like someone else. Not all go through the long process that Gass says he endured, but each must visit the Registry with proof of their identity. Massachusetts began using the software after receiving a $1.5 million grant from the US Department of Homeland Security as part of an effort to prevent terrorism, reduce fraud, and improve the reliability and accuracy of personal identification documents that states issue."

Face Recognition Result and Discussion Part 1/4

CHAPTER 4

RESULT AND DISCUSSION

4.1 Introduction

This chapter described the results produced based on the methodology explained in Chapter 3. The results are shown and discussions are provided for each experiment. These experiments are divided into three (3) main parts Principal Components Analysis, training and recognition result and experimental result. Prototype model that is designed for this research purpose is also demonstrated in this chapter. 




4.2 Principal Component Analysis


Sample face images from ORL face dataset is shown in Figure 4.1 respectively. The sample showed seven different persons with different conditions. For easy explanations only three face images from each class or persons is taken as training set. Thus, 21 face images is used as a training set and 49 face images as testing set. The training set is then converted into a big matrix,  with its size  where m is the number of training set and P is equal to number of pixels of each face image.

 Figure 4.1: Example ORL dataset

 

Biometric Recognition Methodology part 4/4 - Normalization

3.6 Normalization


Since the value of Backpropagation neural network required input range from zero (0) to one (1) as used the sigmoid activation function and it is found that most of the result produced in feature extraction using eigenfaces are not in particular range, thus the normalization process are required. In these proposed neural network, three types of different normalizations techniques had been selected for this reason [Puteh Saad, 2001]. They are the simple unit range (SUR), improve unit range (IUR) and improved linear scaling (ILS). Equation (3.42), (3.43) and (3.44) shows the computation needed for SUR, IUR and ILS respectively. The best technique adopted is based on the highest classification rate produced by backpropagation neural network.

With reference to the above equation  refers to the new value of feature,  in each dimension after the normalization process. Furthermore xmax and xmin refer to the maximum and minimum features value respectively. For the ILS computation,  refers to the mean of the feature and  is used for the standard deviation of the features in the same dimension. The dimensions for each vector are defined as .

Biometric Recognition Methodology part 3/4 - Artificial Neural Network Implementations

3.4 Artificial Neural Network Implementations


The capability of neural network to differentiate patterns make the backpropagation neural network is chosen to classify unknown face images. In the thesis, the proposed system implemented the binary sigmoid function in training phase. The binary sigmoid has a normalized range within 0 to 1 which can be described as;

    (3.28)

where c controls the firing angle of the sigmoid.

 Figure 3.8: The sigmoid activation function with different values of c

From figure 3.8 when c is large, the sigmoid becomes like a threshold function and when c is small, the sigmoid becomes more like a straight line (linear). If value of c is large, the learning faster but a lot of information is lost. However, more information is gain although the speed very slow with small amount of c. Because of this function is differentiable, it enables the backpropagation algorithm to adapt the lower layer of weights in a multilayer neural network [Marzuki Khalid, 2005]. This backpropagation algorithm is explained in the following paragraph.

Biometric Recognition Methodology part 2/4 - Feature Extraction

3.3 Feature Extraction

Previously each face image, $\Gamma_i$ of size   is converted into a big matrix where each row, M presented the image and column is P = XY  and revenue difference matrix A with its size (M x P).  This section (Figure 3.5) described the eigenvalues and eigenvectors using Jacobi’s method, dimension reductions, eigenfaces transformations, features vectors representations and how the eigenfaces is used to rebuild the face images.

 Figure 3.5: Diagram for Eigenfaces Formations

Biometric Recognition Methodology part 1/4 - Intro and Preprocessing

METHODOLOGY


                  
                  
3.1 Introduction

  
    This chapter describes the implementation of the chosen method using the suitable theory. Hence the methodology which described how the difference magnificent mathematical is combined together to achieve the research objectives. There are four (4) phases in the proposed face recognition system namely; Preprocessing, Feature Extraction, Training and Recognition. Each phase is briefly described as follows:


a)    Preprocessing. In this phase, the face dataset acquisition and the preprocessing of the face images are performed.


b)    Feature extraction. Those face library images were prepared for the feature extraction phase. This phase is performed to find the useful feature such as eigenvalues $(\lambda_i )$, eigenvectors$(\ev_i )$ , eigenfaces$(\U_i )$  and feature vectors(\Omega) .


c)    Training phase. Trained feature vectors then used for backpropagation neural network training to generalized the neural network weights for recognition phase.


d)    Recognition phase. The set of chosen eigenfaces, feature vectors and neural network weights is then used for recognition phase. The recognition begins by selecting a face image from face library which the system considered the unknown face.
  
Figure 3.1 illustrates the methodology used to recognize an unknown human face. The figure clearly shows where the four phases is located. For the training and recognition, three (3) models are purposed in the research. Each these phase is then described with their algorithm in this chapter.

Figure 3.1: Proposed modeling System

LITERATURE REVIEW PART 3/3 - Artificial Neural Network

2.4 Artificial Neural Network


Artificial Neural Network (ANNs) has a large appeal to many AI researchers. A neural network can be defined as model of reasoning based on the human brain. The brain consists of a closely interconnected set of nerve cells or basic information-preprocessing units, called neurons. The human brain incorporates nearly 10 billion neurons and 60 trillion connections, synapses between them [Shepherd, 1990]. By using multiple neurons simultaneously, the brain can perform its functions much faster than the fastest computers in existence today [Negnevitsky, 2002].

2.4.1 Architecture


A multilayer perceptron is a feed-forward neural network with one or more hidden layers. Typically, the network consists of an input layer of source neurons that at least one hidden layer of neurons and an output layer of neurons (Figure 2.3). The input signals are propagated in a forward direction on a layer-by-layer basis. The backpropagation algorithm perhaps is the most popular and widely used neural paradigm. It based on the generalized delta rule proposed by research group in 1985 headed by Dave Rumelhart based at Stanford University, California, USA.

Figure 2.3: Feed-forward Neural Network

LITERATURE REVIEW PART 2/3 - Principal Component Analysis Approach

2.3 Principal Component Analysis Approach


Principal Component Analysis (PCA) is also known as Karhunen-Loeve transformation or eigenspace projection. It is a well known statistical technique to identify patterns in data. It highlight similarities and differences between patterns. Since patterns can be hard to find in data of high dimension, where the luxury of graphical representation is not available, PCA is a powerful tool to extract patterns. The other main advantage of PCA is that once the pattern is found in the data and the data is compressed where the number of dimensions is reduced, however less information is lost [Smith, 2002].

LITERATURE REVIEW PART 1/3 - Biometric Recognition of Human Face Background

CHAPTER 2




LITERATURE REVIEW

2.1 Introduction  


    The human biometric recognition of human face is a popular research topic in computer vision. Its motivation arises in commercial security system. Despite the fact that other biometric recognition identification methods such as fingerprints and iris scans may more accurate, biometric recognition of human face has always been a major research focus because it is noninvasive and it is natural and intuitive to users.


As the biometric recognition of human face is an application in computer vision, hence the standard methodology of the biometric recognition shown in Figure 2.1. In the preprocessing phase, the unwanted noise or irrelevant data is eliminated from the image. Others preprocessing steps include spatial quantization (reducing the number of bits per pixel) or finding regions of interest. The second stage involves transforming the image data into another domain to extract the significant features. Lastly, the extracts features are examined and evaluated

Figure 2.1: Standard Image Analysis Model

INTRODUCTION OF FACE BIOMETRIC RECOGNITION

Machine biometric recognition of human faces is a challenging problem due to the changes in the face identity and variation between images of the same face due to illumination and viewing direction. The issues are how the features are adopted to represent a face under environmental changes and how the classification is done to a new face image based on the chosen representations.


Principal Component Analysis (PCA) which also known as eigenfaces is used in this research to extract set of feature extraction of the faces. The reason that it is chosen due to its capability to extract the relevant information from high dimensional matrix [Turk, 1991]. As for the classification task, the euclidean distance and backpropagation neural network is chosen since most researchers show superior performance claimed that both methods.


A set of features from a face image is representation using the eigenvalues and eigenvectors. In order to obtain eigenvalues and eigenvectors, Jacobi’s method is used due its accuracy and robustness. As for the classification task, backpropagation algorithm is applied.