Facial recognition

More recently, these authors have reported the synthesis of a new thiophene-based analogue of (I ,i )-Me-DuPHOS called UlluPHOS. The facial recognition and enantioselection associated with ruthenium complexes of UlluPHOS and Me-DuPHOS were shown to be similarly high in various hydrogenations of p-keto esters (Scheme 8.32). The most important difference between these two ligands was found by comparing the reaction rates. Indeed, the authors have observed that the use of UlluPHOS considerably increased the activity of the complexes. 

Scenario Imagine a drive-through fast-food chain that recognizes customers using facial recognition software, and predicts their order based on their... 

Refer to your Process or Value Stream Map. Each key process step should have a row on the Control Plan. In our example, the process of taking a customer s order at the drive-through would entail many process steps—facial recognition, order processing, payment, and so on. We ll fill out our sample Control Plan (Exhibit 55.1) using the process step facial recognition. 

The output is the desired result of the process step. It could be an outcome or event, or it might be the next step in the process. You may have more than one output for a given process step. However, if all your process steps have several outputs, your process steps are probably too high-level. For our drive-through example, if the process step is facial recognition, the outputs are cycle time and accurate identification. 

Facial Recognition Accurate Image 80 - 90% Accuracy 4 a (sigma) Report Software Algorithm Tech Support 1 per Month/ Sample of Stores Contact Vendor Drive-up Plan.doc... 

EXHIBIT 55.1 (Downloadable). This example shows the Control Plan for just one step (facial recognition) from our sample fast-food, drive-through process. 

A Transition Plan includes any additional information, above and beyond the Control Plan, needed to move from a pilot or small-scale production to full-scale production and delivery. Typically this documentation is detailed and so is merely referenced in the Control Plan. In our example, the Transition Plan might include tech support and vendor contact information, and suggested scripts for taking orders or soliciting customers to add to the facial-recognition database. 

Automated data capture is an important aspect in supply chain management and logistics. In the last decade, automated identification and data capture (AIDC) has revolutionized the overall supply chain managementprocess. AIDC includes technology to identify objects, and automatically collects data aboutthem and updates the data into software systems without human intervention. Some examples of AIDC technologies include bar codes, RFID, smart cards, voice and facial recognition, and so forth. 

One example of a pattern recognition machine is a computer capable of facial recognition. The computer first evaluates the face using a visual sensor and then divides the face into parts, including the eyes, lips, and nose. Next, the system assesses the relationship between individual parts, such as the distance between the eyes, and notes features such as the length of the lips. Once the machine has evaluated an individual s face, it can store the data in memory and later compare the information against other facial scans. Facial recognition machines are most often used to confirm identity in security applications. 

NNC is relatively easy to implement in hardware should the need arise. This was, in fact, done by Stonham in the Wisard facial recognition machine. The hardware implementation of NNC and similar algorithms is provided by Anstin (1998). 

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