Encryption of data is translating data to another shape or symbol which enables people only with an access to the secret key or a password that can read it. The data which are encrypted are generally referred to as cipher text, while data which are unencrypted are known plain text. Entropy can be used as a measure which gives the number of bits that are needed for coding the data of an image. As the values of pixel within an image are dispensed through further gray-levels, the entropy increases. The aim of this research is to compare between CAST-128 with proposed adaptive key and RSA encryption methods for video frames to determine the more accurate method with highest entropy. The first method is achieved by applying the "CAST-128" and the second is achieved by applying the "RSA ". CAST-128 utilizes a pair of sub-keys for each round as a quantum of  five bits that was utilized as a key of rotation for each round and a quantum of 32 (bits) was utilized as a key of masking into a round . The proposed adaptive 128-bits key can be extracted from the main diagonal of each frame before encryption. RSA is a public-key cryptographic technique which can be known as (asymmetric) cryptography. An asymmetry of a key depends on factoring a product of two big prime values. A comparison was applied on several videos and the results showed that CAST-128 method proved the highest degree of entropy even if the frames have lots of distorted data or unclear image pixels. For example, the entropy value of a sample of a girl video is 2581.921 when using CAST-128, while it is 2271.329 when using the RSA; also the entropy value of a sample of a scooter video is 2569.814 when using the CAST-128, while it is 2282.844 when using RSA.

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The biometric-based keys generation represents the utilization of the extracted features from the human anatomical (physiological) traits like a fingerprint, retina, etc. or behavioral traits like a signature. The retina biometric has inherent robustness, therefore, it is capable of generating random keys with a higher security level compared to the other biometric traits. In this paper, an effective system to generate secure, robust and unique random keys based on retina features has been proposed for cryptographic applications. The retina features are extracted by using the algorithm of glowworm swarm optimization (GSO) that provides promising results through the experiments using the standard retina databases. Additionally, in order to provide high-quality random, unpredictable, and non-regenerated keys, the chaotic map has been used in the proposed system. In the experiments, the NIST statistical analysis which includes ten statistical tests has been employed to check the randomness of the generated binary bits key. The obtained random cryptographic keys are successful in the tests of NIST, in addition to a considerable degree of aperiodicity.