Transportation security personnel rely on facial photos or "mug-shots" to spot persons of interest within crowds. Passing through a security check point, video displays and printouts of mug-shots are usually low-resolution and in a fixed facial pose. Thus, a database-cuing Aided-Target Recognition, (AiTR) video surveillance system is recommended for prompting the inspector to take a closer look at a specific passenger walking down the corridor. Taking advantage of commercially available Face Detection System on Chips (FD-SOC, 0.04sec/frame), we developed a fast self-reference algorithm to sort facial poses among passengers. We can increase the overlapping POFs (pixels on faces) in matching facial shots at an arbitrary pose, with real-time sorted facial poses. Lemma: Based on the human walking physiology along a corridor, a long time average of frames called the long exposure produces the front view with respect to the camera, while the short exposure is a single facial pose. Then, the fiduciary triangle is defined among two eyes and nosetip of the frontal normal view which may and may not be a right triangle. Theorem Self-Reference Matched Filtering (Szu et al. Opt Comm. 1980; JOSA, 1982) applied to the Facial-Pose problem. If we replace the desirable estimated output of Weiner filter as the long exposure, then the filter can select a short exposure as the normal frontal view. Corollary: Given a short exposure as normal frontal view, the fiduciary triangle can decide all poses. We explain a divide and conquer search algorithm as follows: The algorithm in the first pass through the batch of boxes to estimate the middle frontal pose view by a uniform average through the collection of boxes of faces given by the FD-SOC by the Lemma is the front view w.r.t. the camera. The algorithm applies the long term average to find it's nearest neighboring views. If it happens to be only two, then the algorithm proceeds with these two views, it finds their nearest neighbors. There are four such nearest neighbors. If among these four, two happen to be identical to the first, namely the frontal view, then the algorithm confirms that the other two of the four are the growing chain of the central frontal view. This algorithm carries on in an orderly chain from the left most to right most pose, or vice versa in the second pass.

Electronic mails are adopted worldwide; most are easily hacked by hackers. In this paper, we purposed a free, fast and convenient hybrid privacy system to protect email communication. The privacy system is implemented by combining private security RSA algorithm with specific chaos neural network encryption process. The receiver can decrypt received email as long as it can reproduce the specified chaos neural network series, so called spatial-temporal keys. The chaotic typing and initial seed value of chaos neural network series, encrypted by the RSA algorithm, can reproduce spatial-temporal keys. The encrypted chaotic typing and initial seed value are hidden in watermark mixed nonlinearly with message media, wrapped with convolution error correction codes for wireless 3 rd generation cellular phones. The message media can be an arbitrary image. The pattern noise has to be considered during transmission and it could affect/change the spatial-temporal keys. Since any change/modification on chaotic typing or initial seed value of chaos neural network series is not acceptable, the RSA codec system must be robust and faulttolerant via wireless channel. The robust and fault-tolerant properties of chaos neural networks (CNN) were proved by a field theory of Associative Memory by Szu in 1997. The 1-D chaos generating nodes from the logistic map having arbitrarily negative slope a = p/q generating the N-shaped sigmoid was given first by Szu in 1992. In this paper, we simulated the robust and fault-tolerance properties of CNN under additive noise and pattern noise. We also implement a private version of RSA coding and chaos encryption process on messages.

We have developed a real-time EOIR video counter-jittering sub-pixel image correction algorithm for a single miniUnmanned Air Vehicle (m-UAV) for surveillance and communication (Szu et al. SPIE Proc. V 5439, pp.183-197, April 12, 2004). In this paper, we wish to plan and execute the next challenge----a team of m-UAVs. The minimum unit for a robust chain saw communication must have the connectivity of five second-nearest-neighbor members with a sliding, arbitrary center. The team members require an authenticity check (AC) among a unit of five, in order to carry out a jittering mosaic image processing (JMIP) on-board for every m-UAV without gimbals. The JMIP does not use any NSA security protocol ("cardinal rule: no-man, no-NSA codec"). Besides team flight dynamics (Szu et al "Nanotech applied to aerospace and aeronautics: swarming," AIAA 2005-6933 Sept 26-29 2005), several new modules: AOA, AAM, DSK, AC, FPGA are designed, and the JMIP must develop their own control, command and communication system, safeguarded by the authenticity and privacy checks presented in this paper. We propose a Nonlinear Invertible (deck of card) Shuffler (NIS) algorithm, which has a Feistel structure similar to the Data Encryption Standard (DES) developed by Feistel et. al. at IBM in the 1970's; but DES is modified here by a set of chaotic dynamical shuffler Key (DSK), as re-computable lookup tables generated by every on-board Chaotic Neural Network (CNN). The initializations of CNN are periodically provided by the private version of RSA from the ground control to team members to avoid any inadvertent failure of broken chain among m-UAVs. Efficient utilization of communication bandwidth is necessary for a constantly moving and jittering m-UAV platform, e.g. the wireless LAN protocol wastes the bandwidth due to a constant need of hand-shaking procedures (as demonstrated by NRL; though sensible for PCs and 3 rd gen. mobile phones). Thus, the chaotic DSK must be embedded in a fault-tolerant Neural Network Associative Memory for the error-resilientconcealment mosaic image chip re-sent. However, the RSA public and private keys, chaos typing and initial value are given on set or sent to each m-UAV so that each platform knows only its private key. AC among 5 team members are possible using a reverse RSA protocol. A hashed image chip is coded by the sender's private key and nobody else knows in order to send to it to neighbors and the receiver can check the content by using the senders public key and compared the decrypted result with on-board image chips. We discover a fundamental problem of digital chaos approach in a finite state machine, of which a fallacy test of a discrete version is needed for a finite number of bits, as James Yorke advocated early. Thus, our proposed chaotic NIS for bits stream protection becomes desirable to further mixing the digital CNN outputs. The fault tolerance and the parallelism of Artificial Neural Network Associative Memory are necessary attributes for the neighborhood smoothness image restoration. The assoc...

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A video-based surveillance system for passengers includes face detection, face tracking and face recognition. In general, the final recognition result of the video-based surveillance system is usually determined by the cumulative recognition results. Under this strategy, the correctness of face tracking plays an important role for the system recognition rate. For face tracking, the challenges of face tracking on a moving platform are that the space and time information used for conventional face tracking algorithms may be lost. Consequently, conventional face tracking algorithms can barely handle the face tracking on a moving platform. In this paper, we have verified the state-of-the-art technologies for face detection, face tracking and face recognition on a moving platform. In the mean time, we also proposed a new strategy for face tracking on a moving platform or face tracking under very low frame rate. The steps of the new strategy for face detection are: (1) classification the detected faces over a certain period instead of every frame (2) Tracking of each passenger is equivalent to reconstruct the time order of certain period for each passenger. If the cumulative recognition results are the only part needed for the surveillance system, step 2 can be skipped. In addition, if the additional information from the passengers is required, such as path tracking, lip read, gesture recognition, etc, time order reconstruction in step 2 can offer the information required.